Magnetic resonance imaging in tissue engineering 9781119193227, 1119193222, 9781119193234, 1119193230, 9781119193272, 1119193273

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Magnetic Resonance Imaging in Tissue Engineering

Magnetic Resonance Imaging in Tissue Engineering Edited by Mrignayani Kotecha, Richard L. Magin, and Jeremy J. Mao

This edition first published 2017 © 2017 John Wiley & Sons, Inc. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions. The right of Mrignayani Kotecha, Richard L. Magin, and Jeremy J. Mao to be identified as the author(s) of this work has been asserted in accordance with law. Registered Offices John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA Editorial Office 111 River Street, Hoboken, NJ 07030, USA For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com. Wiley also publishes its books in a variety of electronic formats and by print‐on‐demand. Some content that appears in standard print versions of this book may not be available in other formats. Limit of Liability/Disclaimer of Warranty The publisher and the authors make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties; including without limitation any implied warranties of fitness for a particular purpose. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for every situation. In view of on‐going research, equipment modifications, changes in governmental regulations, and the constant flow of information relating to the use of experimental reagents, equipment, and devices, the reader is urged to review and evaluate the information provided in the package insert or instructions for each chemical, piece of equipment, reagent, or device for, among other things, any changes in the instructions or indication of usage and for added warnings and precautions. The fact that an organization or website is referred to in this work as a citation and/or potential source of further information does not mean that the author or the publisher endorses the information the organization or website may provide or recommendations it may make. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this works was written and when it is read. No warranty may be created or extended by any promotional statements for this work. Neither the publisher nor the author shall be liable for any damages arising here from. Library of Congress Cataloguing‐in‐Publication data has been applied for ISBN: 9781119193357 Cover image courtesy Temel Kaya Yasar Cover design by Wiley Set in 10/12pt Warnock by SPi Global, Pondicherry, India 10 9 8 7 6 5 4 3 2 1

v

Contents List of Plates  xiii About the Editors  xix List of Contributors  xxi Foreword  xxv Preface  xxvii Book Summary  xxxi Part I 

1

 nabling Magnetic Resonance Techniques for Tissue E Engineering Applications  1

Stem Cell Tissue Engineering and Regenerative Medicine: Role of Imaging  3 Bo Chen, Caleb Liebman, Parisa Rabbani, and Michael Cho

1.1 ­Introduction  3 1.2 3D Biomimetics  5 1.3 ­Assessment of Stem Cell Differentiation and Tissue Development  8 1.4 ­Description of Imaging Modalities for Tissue Engineering  8 1.4.1 Optical Microscopy  9 1.4.2 Fluorescence Microscopy  9 1.4.3 Multiphoton Microscopy  11 1.4.4 Magnetic Resonance Imaging  14 Acknowledgments  15 References  15 2

Principles and Applications of Quantitative Parametric MRI in Tissue Engineering  21 Mrignayani Kotecha

2.1 ­Introduction  21 2.2 ­Basics of MRI  25 2.2.1 Nuclear Spins  25

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Contents

2.2.2 Radio Frequency Pulse Excitation and Relaxation  28 2.2.3 From MRS to MRI  31 2.3 ­MRI Contrasts for Tissue Engineering Applications  32 2.3.1 Chemical Shift  33 2.3.2 Relaxation Times—T1 and T2  33 2.3.3 Water Apparent Diffusion Coefficient  36 2.3.4 Fractional Anisotropy  37 2.4 ­X‐Nuclei MRI for Tissue Engineering Applications  38 2.5 ­Preparing Engineered Tissues for MRI Assessment  38 2.5.1 In Vitro Assessment  38 2.5.2 In Vivo Assessment  39 2.6 ­Limitations of MRI Assessment in Tissue Engineering  39 2.7 ­Future Directions  40 2.7.1 Biomolecular Nuclear Magnetic Resonance  40 2.7.2 Cell–ECM–Biomaterial Interaction  40 2.7.3 Quantitative MRI  40 2.7.4 Standardization of MRI Methods for In Vitro and In Vivo Assessment  40 2.7.5 Super‐Resolution MRI Techniques  41 2.7.6 Magnetic Resonance Elastography  41 2.7.7 Benchtop MRI  41 2.8 ­Conclusions  41 References  42 3

High Field Sodium MRS/MRI: Application to Cartilage Tissue Engineering  49 Mrignayani Kotecha

3.1 ­Introduction  49 3.2 ­Sodium as an MR Probe  50 3.3 ­Pulse Sequences  53 3.3.1 Pulse Sequences for Measuring TSC  53 3.3.2 TQC Pulse Sequences for Measuring ωQ and ω0τc  54 3.4 ­Assessment of Tissue‐Engineered Cartilage  55 3.4.1 Proteoglycan Assessment  57 3.4.2 Assessment of Tissue Anisotropy and Molecular Dynamics  60 3.4.3 Assessment of Osteochondral Tissue Engineering  61 3.5 ­Sodium Biomarkers for Engineered Tissue Assessment  63 3.5.1 Engineered Tissue Sodium Concentration (ETSC)  63 3.5.2 Average Quadrupolar Coupling (ωQ)  64 3.5.3 Motional Averaging Parameter (ω0τc)  64 3.6 ­Future Directions  64 3.7 ­Summary  64 References  65

Contents

4

SPIO‐Labeled Cellular MRI in Tissue Engineering: A Case Study in Growing Valvular Tissues  71 Elnaz Pour Issa and Sharan Ramaswamy

4.1 ­Setting the Stage: A Clinical Problem Requiring a Tissue Engineering Solution  71 4.2 ­SPIO Labeling of Cells  72 4.2.1 Ferumoxides 72 4.2.2 Transfection Agents  73 4.2.3 Labeling Protocols  75 4.3 ­Applications  76 4.3.1 Traditional Usage of SPIO‐Labeled Cellular MRI  76 4.3.2 SPIO‐Labeled Cellular MRI in Tissue Engineering  76 4.4 ­Case Study: SPIO‐Labeled Cellular MRI for Heart Valve Tissue Engineering  77 4.4.1 Experimental Design  77 4.4.2 Potential Approaches—In Vitro  78 4.4.3 Potential Approaches—In Vivo  81 4.5 ­Conclusions and Future Outlook  83 Acknowledgment  84 References  84 5

Magnetic Resonance Elastography Applications in Tissue Engineering  91 Shadi F. Othman and Richard L. Magin

5.1 ­Introduction  91 5.2 ­Introduction to MRE  93 5.2.1 Theoretical Basis of MRE  94 5.2.2 The Inverse Problem and Direct Algebraic Inversion  96 5.2.3 Direct Algebraic Inversion Algorithm  101 5.3 ­Current Applications of MRE in Tissue Engineering and Regenerative Medicine  108 5.3.1 In Vitro TE μMRE  108 5.3.2 In Vivo TE μMRE  110 5.4 ­Conclusion  114 References  114 6

Finite‐Element Method in MR Elastography: Application in  Tissue Engineering  117 Yifei Liu and Thomas J. Royston

6.1 ­Introduction  117 6.2 ­FEA in MRE Inversion Algorithm Verification  118 6.3 ­FEM in Stiffness Estimation from MRE Data  120 6.4 ­FEA in Experimental Validation in Tissue Engineering Application  121

vii

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Contents

6.5 ­Conclusions and Discussion  Acknowledgment  125 References  125 7

124

In Vivo EPR Oxygen Imaging: A Case for Tissue Engineering  129 Boris Epel, Mrignayani Kotecha, and Howard J. Halpern

7.1 ­Introduction  129 7.2 ­History of EPROI  131 7.3 ­Principles of EPR Imaging  132 7.4 ­EPR Oxymetry  134 7.5 ­EPROI Instrumentation and Methodology  135 7.5.1 EPR Frequency  135 7.5.2 Resonators 135 7.5.3 Magnets 136 7.5.4 EPR Imagers  137 7.6 ­Spin Probes for Pulse EPR Oxymetry  138 7.7 ­Image Registration  139 7.8 ­Tissue Engineering Applications  140 7.8.1 EPROI in Scaffold Design  140 7.8.2 EPROI in Tissue Engineering  142 7.9 ­Summary and Future Outlook  142 Acknowledgment  142 References  143 Part II 

8

 issue‐Specific Applications of Magnetic Resonance T Imaging in Tissue Engineering  149

Tissue‐Engineered Grafts for Bone and Meniscus Regeneration and Their Assessment Using MRI  151 Hanying Bai, Mo Chen, Yongxing Liu, Qimei Gong, Ling He, Juan Zhong, Guodong Yang, Jinxuan Zheng, Xuguang Nie, Yixiong Zhang, and Jeremy J. Mao

8.1 ­Overview of Tissue Engineering with MRI  151 8.2 ­Assessment of Bone Regeneration by Tissue Engineering with MRI  152 8.3 ­MRI for 3D Modeling and 3D Print Manufacturing in Tissue Engineering  157 8.4 ­Assessment of Menisci Repair and Regeneration by Tissue Engineering with MRI  161 8.5 ­Conclusion  168 Acknowledgments  168 References  169

Contents

9

MRI Assessment of Engineered Cartilage Tissue Growth  179 Mrignayani Kotecha and Richard L. Magin

9.1 ­Introduction  179 9.2 ­Cartilage  181 9.3 ­Cartilage Tissue Engineering  182 9.3.1 Cells  183 9.3.1.1 Chondrocytes 183 9.3.1.2 Stem Cells  183 9.3.2 Biomaterials  183 9.3.3 Growth Factors  184 9.3.4 Growth Conditions  184 9.4 ­Animal Models in Cartilage Tissue Engineering  184 9.5 ­Tissue Growth Assessment  186 9.6 ­MRI in the Assessment of Tissue‐Engineered Cartilage  187 9.7 ­Periodic Assessment of Tissue‐Engineered Cartilage Using MRI  187 9.7.1 Assessment of Tissue Growth In Vitro  187 9.7.1.1 Accounting for Scaffold in Tissue Assessment  191 9.7.2 Assessment of Tissue Growth In Vivo  191 9.7.3 Assessment of Tissue Anisotropy and Dynamics  193 9.7.3.1 Assessment of Macromolecule Composition  194 9.7.3.2 Assessment of Tissue Anisotropy  198 9.8 ­Summary and Future Directions  199 References  200 10

Emerging Techniques for Tendon and Ligament MRI  209 Braden C. Fleming, Alison M. Biercevicz, Martha M. Murray, Weiguo Li, and Vincent M. Wang

10.1 ­Tendon and Ligament Structure, Function, Injury, and Healing  209 10.2 ­MRI Studies of Tendon and Ligament Healing  211 10.3 ­MRI and Contrast Mechanisms  219 10.3.1 Conventional MRI Techniques  219 10.3.2 Advanced MR Techniques  222 10.4 ­Significance and Conclusion  228 Acknowledgments  228 References  228 11

MRI of Engineered Dental and Craniofacial Tissues  237 Anne George and Sriram Ravindran

11.1 ­Introduction  237 11.2 ­Scaffolds  238 11.3 ­Extracellular Matrix  238 11.4 ­Tissue Regeneration of Dental–Craniofacial Complex  239 11.4.1 Advantages of Using ECM Scaffolds with Stem Cells  240

ix

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Contents

11.4.2 Stem Cells  242 11.5 ­MRI in Tissue Engineering and Regeneration  243 11.5.1 MRI of Human DPSCs  243 11.5.2 MRI of Tissue‐Engineered Osteogenic Scaffolds  244 11.5.3 MRI of Chondrogenic Scaffolds with Cells In Vitro  244 11.5.4 MRI of Chondrogenic Scaffolds with Cells In Vivo  245 11.5.5 MRI Can Differentiate Between Engineered Bone and Engineered Cartilage  246 11.5.6 MRI to Assess Angiogenesis  246 11.6 ­Challenges and Future Directions for MRI in Tissue Engineering  246 Acknowledgments  247 References  247 12

Osteochondral Tissue Engineering: Noninvasive Assessment of Tissue Regeneration  251 Tyler Stahl, Abeid Anslip, Ling Lei, Nilse Dos Santos, Emmanuel Nwachuku, Thomas DeBerardino, and Syam Nukavarapu

12.1 ­Introduction  251 12.2 ­Osteochondral Tissue Engineering  252 12.2.1 Osteochondral Tissue  252 12.2.2 Biomaterials/Scaffolds 252 12.2.3 Cells 255 12.2.4 Growth Factors  256 12.3 ­Clinical Methods for Osteochondral Defect Repair and Assessment  257 12.3.1 Diagnostic Modalities  257 12.3.2 Treatment Methods  260 12.3.2.1 Microfracture  260 12.3.2.2 Autografts and Allografts  260 12.3.2.3 Tissue Engineering Grafts  262 12.4 ­MRI Assessment of Tissue Engineered Osteochondral Grafts  262 12.4.1 In Vitro Assessment  263 12.4.2 In Vivo Assessment  264 12.5 ­MRI Assessment Correlation with Histology  264 12.6 ­Conclusions and Challenges  265 Acknowledgments  265 References  265 13

Advanced Liver Tissue Engineering Approaches and Their Measure of Success Using NMR/MRI  273 Haakil Lee, Rex M. Jeffries, Andrey P. Tikunov, and Jeffrey M. Macdonald

13.1 ­Introduction  273 13.2 ­MRS and MRI Compatibilization—Building Compact RF MR Probes for BALs  278

Contents

13.3 ­Multinuclear MRS of a Hybrid Hollow Fiber–Microcarrier BAL  280 13.3.1 Viability by 31P MRS  282 13.3.2 Quantifying Drug Metabolic Activity and Oxygen Distribution by 19F MRS  284 1 13.4 H MRI of a Hollow Fiber Multicoaxial BAL  286 13.4.1 BAL Integrity and Quality Assurance  286 13.4.2 Inoculation Efficiency and Prototype Redesign Iteration  288 13.4.3 Flow Dynamics  289 13.4.4 Diffusion‐Weighted and Functional Annotation Screening Technology (FAST) Dynamic Contrast MRI  291 13.5 ­Magnetic Contrast Agents Used in MRI of Liver Stem Cell Therapy  293 31 13.6 P and 13C MRS of a Fluidized‐Bed BAL Containing Encapsulated Hepatocytes  294 13.6.1 31P MRS Resolution, SNR, Viability, and pH  296 13.6.2 13C MRS to Monitor Real‐Time Metabolism  296 13.7 ­Future Studies  298 13.7.1 Dynamic Nuclear Polarization  298 13.7.2 Constructing Artificial Organs  300 13.8 ­Discussion  301 Acknowledgment  303 References  303 14

MRI of Vascularized Tissue‐Engineered Organs  311 Hai‐Ling Margaret Cheng

14.1 ­Introduction  311 14.2 ­Importance of Vascularization in Tissue Engineering  312 14.3 ­Vessel Formation and Maturation: Implications for Imaging  314 14.4 ­Imaging Approaches to Assess Vascularization  317 14.5 ­Dynamic Contrast‐Enhanced MRI for Imaging Vascular Physiology  318 14.6 ­Complementary MRI Techniques to Study Vascularization  321 14.7 ­Considerations for Preclinical Models and Translation to Clinical Implementation  325 14.8 ­Future Directions  326 14.9 ­Conclusions  327 References  327 15

MRI Tools for Assessment of Cardiovascular Tissue Engineering  333 Laurence H. Jackson, Mark F. Lythgoe, and Daniel J. Stuckey

15.1 ­The Heart and Heart Failure  333 15.2 ­Cardiac Engineering and Cell Therapy  334 15.3 ­Imaging Heart Failure  336

xi

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Contents

15.3.1 Cine MRI  336 15.3.2 Regional Heart Function  338 15.3.3 Viability Imaging  340 15.3.4 Relaxometry and Parametric Imaging  342 15.3.5 Myocardial Perfusion Imaging  344 15.4 ­Imaging Cardiac Regeneration  346 15.5 ­Monitoring Cardiac Regeneration  348 15.5.1 MRI to Track Stem Cells  348 15.5.2 MRI to Track Engineered Tissues  353 15.6 ­Translational Potential and Future Directions  355 References  357 16

Peripheral Nerve Tissue Engineering and Regeneration Observed Using MRI  367 Shan‐Ho Chan and Shan‐hui Hsu

16.1 ­Introduction  367 16.2 ­Receiver Coils Commonly Applied in Nerve Tissue Engineering  368 16.3 ­Various Tools for Real‐Time Monitoring of the Nerve Regeneration  368 16.4 ­Current Materials, Methods, and Concepts in Peripheral Nerve Repair  368 16.5 ­MRI Parameters in Peripheral Nerve Tissue Engineering  371 16.6 ­Advantages of Real‐Time Monitoring of Nerve Regeneration Using MRI  373 16.7 ­Choosing Animal Models for MRI Studies of Peripheral Nerve Tissue Engineering  374 16.8 ­Imaging Ability Through Nerve Conduits of Peripheral Nerve Tissue Engineering  375 16.9 ­Further Imaging Functions of MRI in Peripheral Nerve Tissue Engineering  376 16.10 ­Tractography in Peripheral Nerve Tissue Engineering  376 16.11 ­Novel Contrast Agents  378 16.12 ­Conclusions  378 References  379 Index  383

xiii

List of Plates Figure 1.3  Cell migration into a synthetic three‐dimensional (3D) scaffold. A composite 3D scaffold composed of poly(methacrylic acid) (PMMA) and poly(hydroxyethyl methacrylate) (PHEMA) was developed for cornea tissue engineering. Confocal microscopy was used to monitor migration of corneal fibroblasts into the acellular scaffold. Using a viability assay, the live or dead cells fluoresce different in different wavelengths. Because the confocal images were collected at different focal depths, reconstructed 3D images can be produced with detailed information in the direction of cell migration into the scaffold. * indicates p 1 mm, respectively [31, 32].

1.4 ­Description of Imaging Modalities for Tissue Engineering There are numerous imaging modalities that have been applied to characterize the engineered tissue constructs. It is not our aim here to discuss exhaustively each of the imaging techniques. Rather, the goal is to outline several different

1.4 ­Description of Imaging Modalities for Tissue Engineerin

modalities that offer advantages and disadvantages and therefore provide some pertinent information to the investigator to select imaging modalities that are most appropriate for particular tissue constructs. It is important to note that the researcher is not limited to using only one imaging modality but instead has the option of incorporating multiple different modalities (e.g., multimodal imaging) to determine, for example, the viability and functionality of the engi­ neered tissue constructs, both 2D and 3D scaffolds. 1.4.1  Optical Microscopy

This simple imaging modality requires no complicated instrumentation, and compound lenses along with a light source are sufficient to form images of small‐scale objects such as living cells and tissues. The living objects, some­ times referred to as phase objects, tend not to alter the amplitude of the inci­ dent light because they do not typically absorb light, but rather diffract light and cause phase shift in the light rays passing through them. Based on this principle, phase microscopy was invented by Zernike and he won the Nobel Prize in 1953 for this work [33, 34]. Phase microscopy has been used for more than 50 years and yet provides a useful imaging tool to visually inspect, exam­ ine, and determine some of the cellular behaviors and response in intact speci­ men. This noninvasive and labeling‐free imaging modality nonetheless suffers from a number of disadvantages. Optics physics dictates that the resolution is inherently restricted to ~λ/2, where λ is the wavelength of the illuminating light source. For example, if one were to apply a green light source (λ = 500 nm), the theoretical resolution would be limited to 250 nm. While cells and internal organelles of microns in size are identified and visualized using phase micros­ copy, the resolution is typically much lower due to imperfect lenses. Moreover, the phase images do not provide accurate assessment along the vertical direc­ tion (e.g., Z‐axis). When applied with polarized light source and detection, differential interference contrast (DIC) microscopy can produce images with 3D appearance [35]. The Nomarski optics is now widely available to generate 3D‐like images but do not offer the capability to quantify the images in the vertical direction. Generally, phase microscopy is useful for visual examination and some quantitative image analyses but is not sufficient to elucidate with molecular details. 1.4.2  Fluorescence Microscopy

One of the most useful imaging modalities that is still heavily applied in bio­ medical research today is referred to as fluorescence microscopy. It does not rely on the phase image formation but rather uses molecular compounds (e.g., fluorescent dyes) that, upon excitation by an external light, emit light signals in the visible wavelength range. This imaging technique can be applied to eluci­ date cellular and molecular mechanisms. Because the fluorescent dyes them­ selves act as a light source of the objects being imaged, the λ/2 theoretical

9

10

1  Stem Cell Tissue Engineering and Regenerative Medicine: Role of Imaging

resolution limit is no longer applicable [36], but instead the resolution is ­primarily determined by the microscope objective used to collect the light ­signals (e.g., magnification and numerical aperture) and the characteristics of light detecting devices (e.g., charge‐coupled device or CCD camera). This imaging modality offers various applications relevant to tissue engineering. Ion specific dyes (e.g., Fluo‐4 to bind to free Ca2+) are routinely used to character­ ize and determine the functionality of excitable cells such as neurons and car­ diomyocytes. Novel fluorescent dyes are being continuously developed that allow conjugation of antibodies with fluorophores, and therefore antibody binding can be visualized. Because the fluorescent signal is proportional to the extent of molecular interactions in the liner regime, it can be calibrated to quantify, for example, the free Ca2+ concentration, protein expressions, or ­protein–protein association [37, 38]. Unlike phase microscopy, fluorescence imaging requires molecular/chemical compounds to be introduced to the specimen. While there are thousands of different fluorophores already deve­ loped and readily available, validated, and extensively applied, they are none­ theless chemical species that might interfere with the intended observations. Phototoxicity [39], fluorophore bleaching [40], and quenching due to excess fluorophore concentration [41] could affect the specimen and generate artifacts. Conservation of energy demands that the emitted fluorescence signals have longer wavelengths than the excitation lights (i.e., known as Stoke’s shift); therefore, well‐designed bandpass optical filters are crucial for proper use of fluorescence microscopy. Similar to phase microscopy, the conventional fluo­ rescence imaging suffers from the lack of resolution in the Z‐axis. Signals detected by the pixels in a camera would have typically been integrated in the vertical direction; therefore, the accumulated signals are reported to the researcher without spatially resolved information in the vertical direction. This challenging technical difficulty was mitigated by the invention of confocal flu­ orescence microscopy. It utilizes a physical pinhole and a stepping motor to discriminate emitted light signals that originate from the out‐of‐focus planes [42], allowing a stack of 2D images acquired at different depths of the speci­ men. Many algorithms have been developed to combine stacked 2D images and generate reconstructed 3D images. Since the area of fluorescence signal collection is greatly diminished by the size of the pinhole, many confocal imag­ ing systems employ photomultiplier tubes (PMTs) instead of CCD cameras. With easy access to lasers, laser scanning confocal instruments are preferred that can precisely control the spatial movement and increase the signal‐to‐ noise ratio for better quality imaging. Spatial scanning demands time and image acquisition is slowed. We have applied confocal fluorescence micros­ copy to determine the cell penetration depth into a hybrid composite scaffold of PMMA–PHEMA. Migration of corneal fibroblasts into the acellular com­ posite scaffold over a 2‐week period of time demonstrates that the cells can penetrate into the scaffold ~70 µm within the first week and a longer incuba­ tion time did not increase the cell penetration depth (Figure 1.3).

1.4 ­Description of Imaging Modalities for Tissue Engineerin

100 100 µm *

80 Day 4

100 µm

Depth (µm)

Day 4

*

60

40

Day 8 20

Day 8 100 µm

0

4

8

13

Day

Day 13

Figure 1.3  Cell migration into a synthetic three‐dimensional (3D) scaffold. A composite 3D scaffold composed of poly(methacrylic acid) (PMMA) and poly(hydroxyethyl methacrylate) (PHEMA) was developed for cornea tissue engineering. Confocal microscopy was used to monitor migration of corneal fibroblasts into the acellular scaffold. Using a viability assay, the live or dead cells fluoresce different in different wavelengths. Because the confocal images were collected at different focal depths, reconstructed 3D images can be produced with detailed information in the direction of cell migration into the scaffold. * indicates p  0)

(e)

μ

z y

(d)

x

B0

(c)

z y

Beff

–1/2 B0 E

ΔE = γħB0

μ

Beff y

+1/2 B0

x

μ

x

B0 typically from 1.5 to 11.7 T

Figure 2.3  Schematic diagram depicting the basic principle of magnetic resonance (MR). Nuclei with spins can be detected using MR. (a) Precessing magnetic moment μ with Larmor frequency ω0 = γB0 in a magnetic field B0. B0 is typically in the range of 1.5–7 T for clinical applications and from 9.4 to 21 T for preclinical applications. (b) In a magnetic field, randomly directed nuclear magnets orient themselves toward the magnetic field. A large number of precessing nuclei creates a nuclear magnetization vector, M. (c) Splitting of nuclear Zeeman sublevel for spin 1/2 nucleus as a function of magnetic field B0. (d) μ precesses around the effective magnetic field when another magnetic field B1 is applied along the y‐axis, (e) when the frequency of B1 matches ω0, precessing μ flips to the antiparallel position (high energy state) with respect to B0 creating resonance absorption.

2.2 ­Basics of MR

A nuclear spin state, with spin quantum number I, can degenerate into (2I + 1) states in the presence of a magnetic field. For example, a spin 1/2 nucleus such as 1H or 13C will split into two levels, whereas a nucleus with spin 3/2 such as sodium will split into four levels. This splitting of nuclear energy states is called “nuclear Zeeman splitting” and is a function of an applied magnetic field (ΔE = ħγB0) as shown in Figure 2.3c. The number of nuclei in these states follows the Boltzmann equation: N N

1/2 1/2

e

E

kT

(2.2)

Here, N−1/2 and N+1/2 are the number of nuclei in higher and lower energy states, respectively. ΔE is the separation of energy states, k is the Boltzmann constant, and T is the absolute temperature in kelvin. It is clear from Equation 2.2 that a higher magnetic field will generate a higher energy split and, thus, will generate a higher signal because of a high initial value of the magnetization vector, M. This fact is the main driver of the rush toward higher magnetic field strengths in NMR and MRI. If a time‐dependent magnetic field, B1, is applied at a right angle to the main magnetic field, B0, nuclei effectively precess around an effective field Beff. The magnetization vector M follows the rate equation as given below:



dM dt

M Beff , with Beff

z B0

0

xB1 (2.3)

When the frequency of B1 matches with the original nuclear precession frequency, resonance absorption occurs. The resonance condition makes the nuclei absorb energy from the external magnetic field. It can flip nuclear spins from a lower energy level (pointing toward the main magnetic field B0) to a higher energy level (pointing opposite to main magnetic field B0) as shown in Figure 2.3d and e. The resonance condition that makes the maximum absorption of energy from the external field is given by the following equation:

0

B0 (2.4)

If we place a detector at the right angle to the direction of B0, we can observe the resonance signal, called the MR signal. This signal is typically detected by an induced voltage in an rf coil placed perpendicular to the main magnetic field (x–y‐plane in Figure 2.3a). The beauty of the MR phenomenon is that the actual resonance frequency ω0 is a function of the chemical environment around the nucleus. This is because each nuclear magnet is affected by the surrounding electron cloud and other nuclear magnets in its vicinity. For example, the protons in CH, CH2, CH3, or

27

28

2  Principles and Applications of Quantitative Parametric MRI in Tissue Engineering

OH will have slightly different resonance frequencies in a magnetic field and can therefore be separated in an NMR spectrum. This is the basis of nuclear magnetic resonance spectroscopy (MRS). Some common biologically relevant nuclei are listed in Table 2.1 along with their MR properties (γ, μ, I, ω0, relative SNR). As we can see from the table, protons have the highest sensitivity. Since most human tissues have a high water concentration, typical MRI involves observation of water protons in the body. The MR properties of water protons that are observable through MRI depend on the tissue where water resides. Typical MRI of engineered tissues also involves the observation of water protons. However, sodium (23Na), phosphorous (31P), fluorine (19F), and carbon (13C) MRS and MRI also have been used to assess engineered tissues. Other nuclei such as oxygen (17O), nitrogen (15N), and chlorine (35Cl & 37Cl) MRS/MRI also have the potential to unlock some of the many mysteries of tissue growth. 2.2.2  Radio Frequency Pulse Excitation and Relaxation

The B1 field for creating the condition of MR is typically applied using an rf pulse of short duration, τ. The application of B1 turns the magnetization vector M away from its equilibrium direction in the z‐axis by an angle γB1τ (Figure 2.4a). If the magnitude of B1 and the duration of τ are such that the magnetization vector M is tilted by 90°, then the rf pulse is called the 90° pulse. Similarly, the rf pulse that tilts the magnetization vector M by 180° is called the 180° pulse. When the 90° rf pulse is applied, the magnetization vector M moves to the xy‐plane as shown in Figure 2.4a and maximum signal is recorded in the xy‐ plane. At this time, all spins are coherently precessing in the same phase. Once this external field is turned off, the signal decays due to the dephasing of spins in the plane with a time constant T2*. The time constant where the absorbed energy is transferred within the spins is called spin−spin relaxation time, T2. The relationship between T2* and T2 is given by 1 * T 2

1 T2

1 (2.5) T2

where T2 is the relaxation or spin dephasing caused by magnetic field inhomogeneity and magnetic susceptibility effects. After the application of a 90° pulse, the z‐component of magnetization is zero, as shown in Figure 2.4a. The energy absorbed by the spin system during the pulse is transferred to degrees of freedom other than the spins (lattice), due to various interactions. Due to this transfer of energy, the magnetization vector slowly recovers its original position with a time constant called spin−lattice relaxation time, T1.

H

2

P

Cl

24.23

75.77

~100

~100

~100

0.04

0.37

1.1

3/2

3/2

1/2

3/2

1/2

5/2

1/2

1/2

1

1/2

I

0.6841

0.8218

1.1317

2.2174

2.6288

−1.8937

−0.2831

0.7024

0.8574

2.7928

μ (μN)

1.406

1.689

17.251

11.262

39.948

−5.774

−4.317

10.708

6.536

42.577

γ (MHz/T)

40.78

48.99

202.61

132.26

470.47

67.78

50.68

125.73

76.75

500.00

At 11.74 T (MHz)

f0 (= ω0/2π)

1.982e−04

3.134e−04

0.1045

0.0360

0.8527

0.0068

0.0033

0.0317

0.0092

1

Relative SNR (for an equal number of nuclei at constant B0)

—

—

Chemical shift, T1, T2

Signal intensity, T1, T2, TQ

Chemical shift, T1, T2

—

—

Chemical shift, T1, T2

—

T1, T2, ADC, FA, T1rho, MT, DQ

MR contrasts in tissue engineering applications

The last column represents the current state of MRS/MRI as applied to tissue engineering. The abbreviations and symbols in the table are as follows: I, nuclear spin quantum number; μ, nuclear magnetic moment (in nuclear magneton, μN); γ, gyromagnetic ratio; and 0 B0 . The relative signal‐to‐noise 5/2 (B0 )3/2 . T1, spin−lattice relaxation time; T2, spin−spin relaxation time; ADC, ratio relative to 1H is calculated using the following equation: SNR water apparent diffusion coefficient; FA, fractional anisotropy; T1rho, T1 relaxation in rotating frame; MT, magnetization transfer coefficient; DQ, double‐quantum coherence; and TQ, triple‐quantum coherence.

37

Cl

35

31

Na

23

F

19

O

17

N

15

C

0.015

~100

H

1

13

Natural abundance (%)

Nuclear isotope

Table 2.1  MR properties and approximate sensitivity of some common biologically relevant nuclei [34, 38].

2  Principles and Applications of Quantitative Parametric MRI in Tissue Engineering

(a)

(b) Signal intensity (a.u.)

z B0

α = γB1τ

M

x

100 80 60 T1 = 0.4 s T1 = 0.8 s T1 = 1.6 s

40 20 0

0

0.4 0.8 1.2 1.6 Time (s)

y

B1

Mz = M0 1–e

–t

2

T1

(c) Signal intensity (a.u.)

30

100

T2 = 40 ms T2 = 80 ms T2 = 160 ms

80 60 40 20 0

0

40

80 120 160 200 Time (ms)

Mxy = M0 e

–t

T2

Figure 2.4  (a) Schematic diagram showing the effect of rf pulse on magnetization vector M and its recovery through relaxation processes. (b) Recovery of Mz for three different systems with different T1; here M0 is the magnetization vector at time t = 0. (c) Recovery of Mxy for three different systems with different T2.

If a 90° pulse is applied, the time course of the z‐component of the magnetization vector will be given by Mz M0 [1 e t /T1 ]; whereas if a 180° pulse is applied, the recovery of Mz will follow this equation, Mz M0 [1 2e t /T1 ]. The decay of magnetization in the xy‐plane is always faster or equal to the recovery of magnetization in the z‐axis; therefore, T2 is always smaller than or equal to T1 (T2 ≤ T1). The Bloch equation with relaxation generalizes the relaxation behavior of magnetization given as follows: dM dt

M t

B t

Mx t

My t T2

Mz t T1

M0

(2.6)

2.2 ­Basics of MR

2.2.3  From MRS to MRI

So far, we have used nuclear magnetization and rf pulses to create a signal (Mxy); however, this signal does not contain any spatial information about the spins because the magnetic field (B0) acting on the spins is homogeneous. In order to create an image, we need to pinpoint the locations of nuclear spins. The localization of nuclear spins is achieved via magnetic field gradients. By varying the magnetic field gradients along x, y, and z axes, one can achieve the full localization of nuclear spins within the sample space. The magnetic field at any location is then given by B x, y, z , t

B0

x Gx t

y Gy t

z Gz t

(2.7)

where Gx, Gy, and Gz are gradients along the x, y, and z axes. Using Equation 2.4, this makes resonance frequency and magnetization position dependent. MRI uses timed pulse sequence schemes to achieve the localization of nuclear spins and the generation of signal via rf excitation. An example of spin‐echo pulse sequence is given in Figure 2.5. This is coded in the form of k‐space data that can be decoded using fast Fourier transform (FFT). Pulse sequences are designed to manipulate water proton magnetization and measure specific parameters of interest such as proton density; chemical shifts, T1, T2, and T1rho; and water diffusion coefficients that reflect the tissue’s microstructure and biochemical properties in engineered tissues.

TR 180°

90° RF

Relaxation delay

90°

TE/2 Slice selection Phase encoding Freq. encoding (read out)

Figure 2.5  An example of spin‐echo MRI pulse sequence for generating proton density, T1‐ and T2‐weighted images. The signal intensity is a function of echo time TE and repetition time TR.

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2  Principles and Applications of Quantitative Parametric MRI in Tissue Engineering

2.3 ­MRI Contrasts for Tissue Engineering Applications Table 2.2 gives an example of tissue‐specific assessment queries in the case of cartilage, bone, pancreas, tendon, ligament, heart, brain, kidney, and liver. MRI has an array of contrast mechanisms that can be employed in answering these inquiries. The common concerns for most engineered tissues are the diffusion of oxygen and nutrients, cellular metabolism, and ECM formation. The tissue‐specific concerns are matrix integrity, mechanical strength, angiogenesis, perfusion, etc. In most natural tissues, the delivery of nutrients and removal of waste are maintained by a bed of microvascular capillaries; therefore, the assessment of angiogenesis is an important assessment criteria for most engineered tissues. In the following sections, we will discuss some common MR contrasts that have been applied in tissue engineering. This list is not exhaustive by any means, but it covers the most common MR contrasts applied in the assessment of engineered tissues. Other chapters in the book cover some of the more exotic contrast mechanisms in detail: for example, Chapter 4 covers the principles of magnetic nanoparticle cell labeling—a technique used for cell visualization; Chapters 5 and 6 cover the basic principles of acquisition and image reconstruction in magnetic resonance elastography (MRE)—a technique used to map the tissue stiffness; Chapter  9 covers the T1rho and MT contrasts; and Chapter 15 covers the basics of MR angiography for blood ­vessel visualization.

Table 2.2  Some common queries for assessment of engineered tissues. Tissue/organ type

Assessment queries

Cartilage

Proteoglycans and collagen type II synthesis, mechanical strength, viscoelastic properties

Bone

Mineralization, bone matrix synthesis, mechanical strength, angiogenesis

Pancreas

Cellular metabolism

Heart

Collagen synthesis, cardiac activity, angiogenesis

Brain

Neuronal activity, angiogenesis

Kidney

Perfusion

Liver

Growth, metabolism, perfusion

Ligament/tendon

Collagen synthesis and orientation

2.3 ­MRI Contrasts for Tissue Engineering Application

2.3.1  Chemical Shift

As stated earlier, the actual resonance frequency for a nuclear spin within a molecule depends on the other nuclei in its vicinity. This is because within any system, the nuclear magnets are affected by surrounding electron clouds and other nuclei in the vicinity that are also magnetic. This gives rise to slightly different resonance frequencies for a nuclear isotope in a molecule when the chemical environment of that isotope is different. For example, protons in CH3CH2OH will have three different resonance frequency regions for protons belonging in CH3, CH2, and OH molecular groups that have different molecular environments. The difference in resonance frequency is measured using a parameter called chemical shifts in an NMR spectrum and is denoted in parts per million (ppm). Most of the current chemical shift data available for engineered tissues are from MRS measurement and lack spatial information. However, it is possible to create voxel‐by‐voxel spectra of engineered tissues using MR spectroscopic imaging (MRSI) [39]. MRSI can be applied in the study of a particular metabolite of interest, for example, alanine, lactate, choline, or pyruvate, and the mapping of these metabolites in entire sample voxel by voxel for the assessment of ECM proteins and cell viability in an engineered tissue [40, 41]. NMR spectroscopy has been extensively used to assess engineered tissues. For example, in (i) cartilage: 1H and 13C NMR for the assessment of proteoglycans and collagen signature molecules [24, 25, 42–44]; (ii) bone: 13C and 31P NMR for collagen formation and inorganic‐to‐organic bone mineral ratio [25, 45, 46]; (iii) pancreas: 31P and 19F NMR for the assessment of dissolved oxygen concentration and cellular metabolism [47]; (iv) liver: 13C, 19F, and 31P for the assessment of growth and metabolism of artificial liver [48]. In addition to tissue growth, NMR spectroscopy can also be applied to assess the suitability of novel polymers for tissue engineering applications, or for tissue growth specific questions. In Figure 2.6, we show an example of 13C NMR chemical shift for the assessment of a polymer leak in the media [49]. 2.3.2  Relaxation Times—T1 and T2

The T1 and T2 weighted MRI of water protons are the most common contrast mechanisms used for the visual assessment of engineered tissues and their integration with the host tissue in vivo. As shown in Figures 2.4b and 2.4c by using symbolic values of T1 and T2 that if a tissue has components with different T1 or T2, it is possible to separate them in an MRI. Based on suitable acquisition parameters, an image can be created with T1 or T2 weighting that can provide a visual clue to the tissue growth. The parametric MRI, where T1 or T2 heat maps are created for the region of interest within the tissue, is a quantitative tissue assessment tool in TERM.

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34

2  Principles and Applications of Quantitative Parametric MRI in Tissue Engineering 6′

4′

5′

Liquid mixture 1′

6

2′

3,3′

1

4

5

2 6

PEGDA H

H2

H2

H2

O

C 2 1 C 6 O 5 Cα C 3 O 4 C Cβ 4 O 3 C Cα 5 β O 6 C CH2 2 C nH H H2 H2 2 O

1 CH2

+

HEMA

4′ CH2

H2

Cα 5′ O 3′ C 1′ C 2′ CH3 OH 6′ Cβ H2

O

PHEMA-PEGDA gel in PBS post 3 day incubation

200 190 180 170 160 150 140 130 120 110 100 90 80 70 60 50 40 30 20 10 ppm

0

Figure 2.6  13C NMR of liquid monomer (PHEMA–PEGDA) mixture and corresponding copolymer in PBS. This copolymer is found to be mechanically tunable and suitable for tissue‐engineering applications. The sharp liquid‐like peak 6 belonging to CH2 chain group from PEGDA is visible for copolymer in PBS. This either indicates that this chain is undergoing extremely fast motion, or it is leaking in the solution. Adapted from Ref. [49].

The advantage of using water relaxation times in tissue assessment is that they are extremely sensitive to the environment surrounding the water molecule. As the tissue matures, the environment around the water changes and this change can be reflected in the water molecule tumbling rate that defines its relaxation times. Figure 2.7 shows an example of the change in relaxation times as a function of the water tumbling rate and the magnetic field strength using the Bloembergen–Purcell–Pound (BPP) theory of relaxation [50]. As shown in the figure, the free water exhibits almost identical T1 and T2 from 2.35 T (1H freq. = 100 MHz) to 16.4 T (1H freq. = 700 MHz) magnetic field strength. However, when the water molecule is in a soft tissue, its tumbling rate decreases which in turn causes T1 and T2 to decrease. In the soft tissue region, T1 and T2 are also field dependent. T1 is the most‐effective relaxation mechanism when the tumbling rate of water molecule matches with the resonance frequency of protons. If the water is in a solid matrix, its motion is severely restricted and far removed from the resonance frequency. In this case, the T1 no longer provides an effective relaxation mechanism for spins. However, T2

2.3 ­MRI Contrasts for Tissue Engineering Application

T2/T1

0.6

T2/T1

0.4

ic

el lg

C on

tro

en og

dr

C ho

Relaxation times (s)

O st e

og

en

ic

0.2

Slow tumbling, water in solids

104

105

106

107

0.83

T1, T2

0.67

T1 at 100 MHz T2 at 100 MHz T2/T1 at 100 MHz T1 at 300 MHz T2 at 300 MHz T2/T1 at 300 MHz T1 at 500 MHz T2 at 500 MHz T2/T1 at 500 MHz T1 at 700 MHz T2 at 700 MHz T2/T1 at 700 MHz

Medium tumbling, water in soft tissues

T2

Fast tumbling, free water

108

109

1010

1011

0.50

T2/T1

1.0

T1

0.33

0.17

1012

Molecular tumbling rate (Hz)

Figure 2.7  The calculated T1 and T2 as functions of the water molecule tumbling rate and magnetic field strength (100–700 MHz) calculated using the BPP theory of relaxation [50]. As the tissue matures, both T1 and T2 decrease, as shown by the red arrow. Most human tissues fall within the range shown by the blue ellipse. T2 is commonly used in the assessment of cartilage regeneration, as can be seen in a recent clinical trial [2, 6]. The figure shows that relaxation times are field dependent for soft tissues. The inset shows an example of how T2/T1 can be used as a unit‐free biomarker for the assessment. As shown in the inset, the control gel has the highest T2/T1, but the ratio is lowest for osteogenic constructs with chondrogenic constructs falling between the two. Data in the inset are adapted from [25, 51]. (See insert for color representation of this figure.)

relaxation is still effective. Therefore, T2 is shorter, while T1 is longer for water in a stiffer matrix or solids. The ratio T2/T1 captures this dynamic of changing tissue maturity more accurately. The ratio T2/T1 is close to 1 for free water and almost 0 for water in solids; however, its value is between 0 and 1 for water in soft tissues that is of interest for tissue engineering assessment. For example, consider the inset in Figure 2.7, where T2/T1 gradually changes from ~0.6 for the collagen/chitosan hydro gel to ~0.26 for osteogenic constructs and further to ~0.31 for chondrogenic constructs after 4 weeks of tissue growth using stem cells seeded in collagen/chitosan hydro gel in respective culture media. Osteogenic constructs are expected to be stiffer than chondrogenic constructs, which is reflected in the T2/T1 ratio. It will be important to explore the T2/T1 ratio further for tissue assessment in different scenarios to see how it is related to the tissue stiffness, an important parameter.

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2  Principles and Applications of Quantitative Parametric MRI in Tissue Engineering

2.3.3  Water Apparent Diffusion Coefficient

Water ADC has also been used extensively for the assessment of engineered tissues. In tissues, the Brownian motion of water is restricted by cellular membranes, intracellular organelles, and extracellular macromolecules. Unlike T1 and T2, the water diffusion coefficient is independent of magnetic field strength, magnetic field inhomogeneity, magnetic susceptibility, and magnetic and electrical interactions between spins. It only depends on the movement of water molecules within the tissue space. In MRI experiments, the water diffusion coefficient is measured using a pair of gradient pulses in a standard MRI experiment. In practice, one or all three gradients can be used to probe the diffusion in the chosen direction, as shown in Figure 2.8. Here, the first gradient pulse magnetically tags the nuclear spins and, after the known time (diffusion time ΔD), the second gradient pulse untags these spins. The resulting signal is a function of the diffusion coefficient, as given by S

S0 e

bD

, with b

2

G2

2

D

3



(2.8)

where γ is the gyromagnetic ratio of water protons, G is the gradient strength, δ is the gradient duration, and ΔD is the delay between the pair of gradients. Water ADC is shown to be sensitive to tissue growth in cartilage and bone tissue engineering [19, 24, 25, 52]. Water ADC is an important indicator of porosity and gives an idea about the flow of nutrients in the system [19]. It also provides an estimate of oxygen tension inside the tissue [53]. Echo

180°

90° RF

Slice selection Phase encoding Freq. encoding

G

δ ΔD

Figure 2.8  Schematic timed diagram of the spin echo–based MRI pulse scheme typically used to measure the water ADC. The diffusion‐sensitive gradient pulses are depicted by rectangular blocks. δ represents the duration, G is the amplitude of the gradient pulses, and ΔD is the delay between the pair of gradients, also known as diffusion time during which the water molecules are allowed to move freely.

2.3 ­MRI Contrasts for Tissue Engineering Application

2.3.4  Fractional Anisotropy

Most tissues in the human body are heterogeneous and anisotropic. In an anisotropic tissue, the water diffusion coefficient is also anisotropic and can be defined by a tensor. A second‐order diffusion tensor or a 3 × 3 covariance matrix represents the water diffusion in anisotropic tissues. The diffusion tensor may be visualized using an ellipsoid whose eigenvectors define the principal axis directions and whose eigenvalues (λ1, λ2, and λ3) define the magnitudes, as shown in Figure 2.9. The movement of water molecules can be described by two metrics, mean diffusivity (MD), and fractional anisotropy (FA), which represent the magnitude and direction of water diffusion, respectively [54, 55]. The FA is given by [56, 57] FA

3 2

1

MD

2

2 2 1

MD

2

2 2

2 3

3

MD

2



(2.9)

Diffusion is isotropic when eigenvalues are equal in all directions and FA will be 0. The highest value of FA, 1.0, represents the unidirectional diffusion as shown in Figure  2.9. The diffusion tensor imaging (DTI) can be used to get information on the direction of water diffusion. Here, the diffusion gradients are applied in at least six directions. DTI studies of muscle indicate FA to be about 0.2–0.4 [58]. In our recent experiment with tissue‐engineered bone and cartilage constructs, we found that tissue‐engineered bone shows higher mean FA values when compared to cartilage constructs grown for the same amount of tissue growth time [59]. In addition to the contrasts discussed before, a number of other water MR contrasts have been used in tissue engineering applications. These include T1rho and MT for assessing tissue‐engineered cartilage [60, 61], dynamic ­contrast‐enhanced MRI (DCE‐MRI) for the assessment of vasculature in t­ issue‐ engineered calvarium bone [62], tissue stiffness contrast measured using MRE (a)

λ1

(b) λ2

λ3 FA = 0

FA = 1

Figure 2.9  Isotropic and anisotropic diffusion within the tissue constructs for random (FA = 0) and oriented collagen fibers (FA = 1).

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2  Principles and Applications of Quantitative Parametric MRI in Tissue Engineering

for the mechanical assessment of tissue‐engineered cartilage [63], and super‐ paramagnetic iron‐oxide (SPIO) labeled MRI for the visualization of chondrocytes in tissue‐engineered cartilage [64]. In a recent publication, extracellular calcium levels were measured using alginate‐coated magnetic nanoparticles via proton MRI [65].

2.4 ­X‐Nuclei MRI for Tissue Engineering Applications As we have seen in the previous sections, protons have the highest sensitivity for MRI; therefore, water proton MRI is the most commonly used method for the assessment of engineered tissues. However, with advancements in hardware and pulse sequences, X‐nuclei such as 13C, 23Na, and 31P MRI have also been applied to the assessment of engineered tissues. These X‐nuclei MRI assessments provide invaluable complimentary information to water proton MRI when assessing engineered tissue. 13C chemical shift and relaxation measurements of different carbons in tissue‐engineered cartilage have been shown to be effective in assessing cell proliferation, chondrocyte metabolism, and collagen dynamics [43]. Phosphorous MRS has been shown to assess bone regeneration in tissue‐engineered bone by measuring organic and inorganic components of bone [45]. Sodium MRI has been particularly useful for the assessment of tissue‐engineered cartilage [51] (see Chapter 3).

2.5 ­Preparing Engineered Tissues for MRI Assessment 2.5.1  In Vitro Assessment

In vitro samples typically do not require any special preparation as long as they are smaller than the rf coil available for imaging. One of the main requirements for achieving high‐resolution and artifact‐free MR imaging is that the sample should be placed at the center of available rf coil. This can be achieved by placing the samples in a MRI compatible tube on top of a magnetic susceptibility matched plug or 1% agar gel as shown in Figure 2.10. Samples can be loaded with the growth culture or with fluorinated silicone oil for measurement. Multiple samples can be stacked in one tube, as shown in the figure. Multiple tubes can be arranged for simultaneous measurement if there is adequate hardware (e.g., gradient coils, rf coil, and wide bore magnet) available to cover them all. MRI provides image resolution of the order of 20–100 µm, with the typical slice thickness in the range of 0.5 mm. Therefore, 2D samples are not practical to measure using this modality.

2.6 ­Limitations of MRI Assessment in Tissue Engineerin

Figure 2.10  A schematic of sample preparation for in vitro tissue assessment in a vertical bore MRI system. The bottom of the tube is filled with a susceptibility‐matched plug or agar gel to keep the samples in the center of the rf coil. The samples are covered with growth media or fluorinated silicone oil to allow for better visualization of the tissue.

MRI compatible tube

Growth media or Fluorinert oil

Engineered tissue

Plug or agar gel

Volume covered by rf coil

2.5.2  In Vivo Assessment

Small animals, such as mice and rats, can be loaded in the rf coil setup along with the vital signs monitoring systems. Most typical animal MRI centers possess a volume coil for high‐resolution whole body measurements of these animals. If the sample is small, for example, a 3–4 mm diameter construct implanted in a rabbit knee, then a suitable surface coil is required for MRI assessment of the implanted tissue.

2.6 ­Limitations of MRI Assessment in Tissue Engineering The main question here is the following: Can MRI replace the histology, microscopy, and biochemical measurements that currently are the backbone of engineered tissue assessment? The short answer is “no” in the near future, and “maybe” in the long term. MR parameters, except for the chemical shift, are indirect measurements of cell and tissue properties. However, MRI provides an invaluable assessment at the tissue level compared to histochemical techniques that provide molecular level assessment. This tissue‐level assessment is important for the understanding the functional aspects of engineered tissues. As discussed earlier, MRI parameters can be directly correlated noninvasively with tissue ECM productions, tissue anisotropy, vasculature, oxygenation, etc. MRI has been shown to provide nearly accurate assessments of tissue growth in many cases. However, its use in a typical tissue‐engineering laboratory is

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2  Principles and Applications of Quantitative Parametric MRI in Tissue Engineering

still limited. The key reasons for this are: (i) low resolution of animal and clinical scanners, (ii) prohibitive cost of using MRI at preclinical settings, (iii) lack of available MRI experts at key facilities who can help with engineered tissue assessments, (iv) indirect association of MRI parameters with tissue growth parameters, and (v) lack of standardization. In Section 2.7, I discuss some key future directions that may drive the field forward.

2.7 ­Future Directions 2.7.1  Biomolecular Nuclear Magnetic Resonance

The recent developments in biomolecular NMR are of interest for their application to tissue engineering problems. The emerging technique of single‐shot, hyperpolarized MRSI is encouraging and could provide a better understanding of tissue growth in real time [66]. Other developments in the field such as improvements in probe and coil design, higher magnetic field strength, optimized pulse sequences, and the development of spin alignment strategies such as hyperpolarization and optical pumping will definitely make the NMR assessment of engineered tissues an invincible tool in the future [67–69]. 2.7.2  Cell–ECM–Biomaterial Interaction

Biomaterials are an important component in the design of engineered tissues. MRS provides an unhindered view of cell–matrix–biomaterial interactions using its array of tools involving different biologically relevant nuclei, as discussed earlier. Further research is needed to uncover how biomaterials interact with cells and ECM to form a new tissue. 2.7.3  Quantitative MRI

The development of parametric MRI is the right step toward quantification of tissue growth processes. Most parametric maps such as T1, T2, or ADC maps provide quantitative information about the tissue microstructure. The maps of individual metabolite during the process of tissue growth will provide us with an invaluable insight into the regeneration process. 2.7.4  Standardization of MRI Methods for In Vitro and In Vivo Assessment

One of the major issues restricting the widespread use of MRI assessment in tissue engineering is the lack of standards in the field. We are currently leading a task group to develop an ASTM standard for engineered cartilage tissue growth assessment using MRI [70]. More such efforts are needed for every tissue being targeted to make MRI an indispensible tool for the assessment of tissue regeneration.

2.8 ­Conclusion

2.7.5  Super‐Resolution MRI Techniques

One of the major limitations of MRI is its low resolution. Most MRIs have a resolution of 100  µm or higher, thus restricting its utility in observing cell‐level changes during tissue growth. Recent developments in diffusion‐based super‐ resolution techniques are very interesting and show a way to obtain MRI data with resolutions in the range of 1–6 µm [71]. 2.7.6  Magnetic Resonance Elastography

One major concern in the development of engineered tissues is the right mechanical properties of the tissue being developed, and the noninvasive assessment of these properties at each stage. MRE combines mechanical vibration with MRI visualization to assess the shear modulus and viscoelastic properties of tissues. MRE has been applied to assess tissue‐engineered cartilage, bone, and adipogenic tissues successfully [63, 72]. Recent developments in the field, such as simultaneous multifrequency multidimensional excitation and combining diffusion, and MRE pulse sequences, are encouraging and will pave the way to faster assessment of engineered tissues in the future [73, 74]. 2.7.7  Benchtop MRI

The prohibitive cost of clinical and animal scanners and the lack of availability of high‐resolution micro‐MRI machines at convenient locations near tissue engineering laboratories are problems inhibiting the widespread use of the MRI assessment of engineered tissues. A benchtop MRI instrument that can be installed in tissue engineering labs is rising up to solve this issue. Benchtop MRI systems can provide a reasonable tissue assessment at low cost at the very place where tissues are being developed [75, 76]. Current magnetic field strength in these systems is in the range of 0.5–1.0 T. A few key players such as Bruker ICON TM and Magritek are in the market.

2.8 ­Conclusions MRI offers an array of contrast mechanisms for tissue assessment at various stages of tissue regeneration. Several potential image biomarkers (T1, T2, ADC, FA, and chemical shift) are well established for the quantitative assessment of tissue regeneration [77, 78]. These contrast mechanisms provide a quantitative assessment of tissue structure, cell density, cell metabolism, ECM production, vascularization, and integration with host tissue. Several new research directions are underway that will improve the sensitivity and specificity of these assessments. These developments will not only bring MRI to all tissue engineering labs enabling the routine noninvasive assessment of tissue growth but also reduce the number of samples and animals needed to test new and developing strategies.

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49

3 High Field Sodium MRS/MRI: Application to Cartilage Tissue Engineering Mrignayani Kotecha Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA Corresponding author email: [email protected]

3.1 ­Introduction The role of sodium ions in maintaining cellular integrity, tissue osmotic b ­ alance, neuronal function, and muscular contraction is well known [1, 2]. These functions are sustained by preserving a precise tissue sodium ion concentration (TSC) in the extracellular space and an active ion gradient between the intracellular and the extracellular space in tissues [2, 3]. As shown in Table 3.1, the TSC is preserved within a tissue but varies from one tissue to another. For example, it is maintained around 50–80 mM in the human brain [4], 240–300 mM in patellar cartilage [5, 6], ~40 mM in the heart [7], ~20 mM in the liver [8], and 25–35 mM in muscle [6]. At the normal physiological level, cells maintain a low sodium concentration (~10 mM) in the intracellular space [2]. The active transportation of sodium ions between the intracellular and extracellular space is important for cell functioning and is channeled by the Na+/K+ ion channel through the cell membrane [1, 9]. The flux in TSC and transmembrane sodium has been associated with various disease pathologies such as muscle contraction, ischemia, hypertension, osteoarthritis, Alzheimer’s disease, and brain‐tumor progression [10, 11]. Sodium (23Na) is an MR active nucleus with 100% natural abundance. Although its concentration in tissues is much smaller (~0.06%) than the proton concentration of tissue water (~100 M/kg/wet weight), it is still higher than other MR active nuclei such as 13C, 31P, 15N, 19F, and 17O (see Chapter 2) [12]. Sodium concentration in tissues varies independently of protons depending upon the physiological and pathological conditions. Therefore, NaMRI is an invaluable noninvasive tool to quantify tissue pathology. The NaMRI has been applied to probe cartilage [10, 13–19], skeletal muscle [6], ischemic heart [7], Magnetic Resonance Imaging in Tissue Engineering, First Edition. Edited by Mrignayani Kotecha, Richard L. Magin, and Jeremy J. Mao. © 2017 John Wiley & Sons, Inc. Published 2017 by John Wiley & Sons, Inc.

50

3  High Field Sodium MRS/MRI: Application to Cartilage Tissue Engineering

Table 3.1  Approximate extracellular physiologic TSC in selected tissues. Tissue

Sodium concentration (mM/kg wet weight)

Cartilage

~240–260

Brain (gray/white matter)

~50–80

Liver

~20

Kidney (cortical/medulla)

~60/100

Heart

~40

Muscle

~25–35

cerebral hemorrhage [20], kidney [21], and skin [22] pathologies in humans. It has also been applied to probe tissue regeneration in cartilage after surgical interventions and tissue‐engineered cartilage [10, 16, 18, 23, 24]. While TSC is an important biomarker and has been extensively exploited, the sodium relaxation times, T1 and T2, are also vital indicators of tissue pathology. The sodium T1 and T2 are 25 times shorter than proton relaxation times because of the efficient and strong quadrupolar relaxation mechanism [7]. The quadrupolar relaxation mechanism is strongly influenced by the environment around the sodium nucleus; therefore, sodium relaxation times are indicative of disease or growth stages. Sodium relaxation times in an anisotropic tissue are biexponential and can be observed using sodium triple‐ quantum coherence (TQC) NMR spectroscopy or imaging. Using biexponential relaxation, we can derive both the average quadrupolar coupling ωQ and the motional parameter ω0τc that define tissue anisotropy and tissue morphology. The ωQ and ω0τc have been applied to determine cartilage degradation in osteoarthritis [25] and to monitor tissue growth of tissue‐engineered cartilage and bone [26, 27]. In the following sections, we will discuss the principles of signal acquisition in sodium TQC filter NMR spectroscopy (NaMRS) and sodium MRI (NaMRI) and outline the application of NaMRS/NaMRI in the assessment of tissue‐ engineered cartilage.

3.2 ­Sodium as an MR Probe As stated before, sodium (23Na) is an MR active nucleus, which means it possesses a nuclear spin with the spin quantum number 3/2 (I = 3/2). Since its spin is greater than 1/2, it also possesses an electrical quadrupolar moment Q at the site of the nucleus as shown in Figure 3.1. The Q arises from the nonspherical distribution of electronic charges around the sodium nucleus. The nuclear spin

3.2 ­Sodium as an MR Prob

I = 1/2

I > 1/2

e2qQ/h = 0

e2qQ/h ≠ 0

Figure 3.1  Schematic diagram depicting the origin of electrostatic interactions in the nucleus with spin I > 1/2 (for sodium nucleus I = 3/2). Positive ellipsoidal nucleus placed in surrounding charges creates a nonzero electrical quadrupole moment. Local charges around the nucleus define the EFGs.

–3/2 Slowrelaxing components T2s, T1s

SQC ST,ω– > ω0

DQC –1/2

Fast-relaxing components T2f, T1f

SQC CT, ω0

TQC +1/2

SQC ST,ω+ < ω0

DQC +3/2

Figure 3.2  Schematic diagram of energy levels and coherences for a sodium nucleus in a magnetic field. The notations in the figure represent: CT, central transition with resonance frequency ω0; DQC, double‐quantum coherence transition; SQC, single‐quantum coherence; ST, single‐quantum satellite transitions with resonance frequencies ω±; TQC, triple‐quantum coherence transition.

can degenerate to 2I + 1 (I = 3/2) Zeeman states in a magnetic field, therefore the sodium nucleus has four separate energy levels in a magnetic field as shown in Figure 3.2 (see Chapter 2). The quadrupolar Hamiltonian that modifies the Zeeman levels is important to the understanding of sodium MR properties in biological tissues and is given by [12] Hˆ Q

eQ Iˆ V 2I 2I 1 

Iˆ

(3.1)

51

52

3  High Field Sodium MRS/MRI: Application to Cartilage Tissue Engineering

where I is the nuclear spin quantum number (I = 3/2 for sodium), Q is the nuclear quadrupole moment, and V(Θ) is the electric field gradient (EFG) tensor, as given by the following equation: V

Vxx 0 0 0 Vyy 0 0 0 Vzz

RQ



RQ



(3.2)

Here, RQ(Θ) is the rotation matrix that defines the relative orientation of the EFG principal axis system and the main magnetic field, and Θ is the molecular orientation. This causes the quadrupolar Hamiltonian to be strongly influenced by the relative orientation of the EFG tensor relative to the magnetic field. The first‐order quadrupolar Hamiltonian that is relevant for sodium ions in biological tissues is given by the following equation: 1 Hˆ Q

Q

where

Q

3 Iˆz2

I I 1 1ˆ 6

(3.3)

3eQVzz 2I 2I 1 

where ωQ is the first‐order quadrupolar coupling and Vzz is the average of EFG component Vzz over all molecular orientations. For the sodium ions in isotropic tissues and liquids, the quadrupolar coupling ωQ averages to zero, thus making the first‐order quadrupolar Hamiltonian Hˆ Q(1) 0. However, when sodium ions are in a highly viscous medium or in an anisotropic biological tissue, such as cartilage or muscle, the Vzz does not average to zero and a residual average quadrupolar coupling ωQ can be observed. The ωQ is a function of molecular orientation and provides information about the directionality or anisotropy in the tissue. In the presence of nonzero quadrupolar coupling, the energy levels are not equally separated as shown in Figure 3.2 [12]. There are 12 allowed and observable transitions in this case: six single‐ quantum (SQ) transitions—two central transitions +1/2 ↔ −1/2 with frequency ω0 and four satellite transitions ±3/2 ↔ ±1/2 with frequencies ω±; the other allowed transitions are multiple quantum transitions such as four double‐ quantum transitions (DQ: ±3/2 ↔ ±1/2) and two triple‐quantum (TQ) transitions (TQ: −3/2 ↔ +3/2) [12]. These allowed transitions result in 12 coherences as shown in Figure  3.2. The SQ central transition is the most commonly observed signal in NaMRI. The fluctuations in time‐dependent EFGs at the site of the nucleus are the dominating mechanisms for relaxation in all quadrupolar nuclei (I > 1/2). These EFG fluctuations are caused by nuclear motion that is influenced by the

3.3 ­Pulse Sequence

environment around the nucleus. Therefore, the spin–lattice relaxation time (T1) and the spin–spin relaxation time (T2) of the sodium nucleus in biological tissues are indicators of a tissue’s microstructure. Nuclear motion in a magnetic field is represented by ω0τc, the motional averaging parameter, where ω0 is the resonance frequency and τc is the correlation time, the time it takes for a nucleus to make one rotation. When the motion is fast and isotropic (ω0τc ≪ 1), such as sodium ions in water, the quadrupolar interaction averages to zero because of the rapid tumbling of sodium ions. In this case, the average quadrupolar coupling ωQ is zero and the transverse and the longitudinal relaxation times are single exponential. The multiple quantum coherence is not observable in such cases. However, when the motion is isotropic without motional narrowing (ω0τc ≈ 1) such as sodium ions in highly viscous liquid or is anisotropic (ω0τc > 1) as with sodium ions in ordered tissues (e.g., cartilage or muscle), the EFGs at the site of the nucleus is nonzero [26]. This is because with the presence of the nonzero EFGs, a small quadrupolar split is responsible for separate central and satellite transitions as shown in Figure  3.2. The sodium relaxation times T1 and T2 are biexponential in this case, with one fast (T1f/T2f ) and one slow component (T1s/T2s) of relaxation times T1 and T2 owing to the satellite (±3/2 ↔ ±1/2) and central transitions (−1/2 ↔ +1/2). Biexponential relaxation creates multiple quantum coherence, such as DQ and TQ, that can be observed using multiple‐quantum coherence spectroscopy, which filters the multiple‐quantum coherence and converts it to the observable SQ transition [10]. Both sodium DQ and TQ coherence spectroscopy can be used in assessing tissue properties. However, TQ spectroscopy has been applied more extensively because of its better signal‐to‐noise ratio (SNR) and better filtration of the isotropic signal [28].

3.3 ­Pulse Sequences 3.3.1  Pulse Sequences for Measuring TSC

The NaMRI typically observes the central transition that is the strongest signal. The sodium MR signal has a lower SNR (~10−5) when compared to protons, therefore most of the current research is focused on improving the SNR. However, the sodium T1 and T2 are approximately 25 times shorter than protons that allow the signal to be acquired 25 times faster, thus recovering some of the lost SNR [7]. The sodium relaxation time T2 is short (~1–3 ms), therefore the signal decays at a much faster rate when compared to the proton signal. This excludes most of the spin echo MRI techniques for signal collection that are commonly used in proton MRI. Since the early days of sodium MRI, gradient echo MRI (a fast acquisition MRI technique that excludes 180° pulse in spin echo pulse sequence)

53

3  High Field Sodium MRS/MRI: Application to Cartilage Tissue Engineering

has been extensively used for measuring TSC [29]. To improve the SNR with the use of a very short echo time (154 mM)

Na+ Na+ Na+ Na+

Impermeable scaffold (no sodium)

Cells (~10 mM)

Na+

Scaffold (~154 mM)

Figure 3.5  Schematic diagram of a sodium MRI voxel in a hypothetical engineered cartilage tissue. Engineered tissues often have a scaffold that may or may not be permeable for sodium ions. Different volume fractions with low‐to‐high sodium concentrations should be taken into account when calculating TSC in engineered tissues. Majumdar [23]. Reproduced with the permission of Springer.

be calculated using the experimentally derived sodium concentration according to the equation: NaTEC

Na exp 1 x

(3.9)

where x is the volume fraction occupied by the scaffold and cells, and Na exp is the experimentally calculated sodium concentration in tissue‐engineered cartilage using the regions of interest (ROIs) (Figure 3.6). Figure 3.6 shows a sample in an MRI tube (Figure  3.6a) and its T2‐weighted proton MRI (Figure 3.6b) and representative examples of sodium MRI maps (Figures 3.6c–e) of poly(lactic‐co‐glycolic acid) (PLGA)‐PuramatrixTM‐based tissue‐engineered cartilage matrix at days 7, 14, and 28. All images were acquired using a 11.74 T (1H frequency = 500 MHz, 23Na frequency = 132 MHz) Bruker microimaging scanner, a 5 mm 23Na/1H double‐tuned RF coil and gradient echo MRI sequence [23]. Using the ROIs for media and samples as shown in Figure 3.6, it is straightforward to calculate the Na media and Na exp. These values were then used to calculate the sodium MRI‐derived FCD using Equations 3.8 and 3.9. Figure 3.7 shows the FCD calculated using sodium MRI method along with the GAG assay‐derived FCD for a period of 4 weeks. As shown in Figures 3.7a and b, even though the GAG amount fluctuates during the growth period, the FCD from the sodium MRI closely follows this trend. Figure 3.7c shows that the sodium MRI‐derived FCDs are highly correlated with the FCDs derived from the GAG assay in tissue‐engineered cartilage [23, 49]. These results show

(a)

(b)

(c) Day 7

(d) Day 14

(e) Day 28

a.u. 3

2

1

4 mm 1H

MRI

23Na

23Na

MRI

MRI

23Na

MRI

Figure 3.6  (a) Schematic of a sample preparation for MRI measurement (the black arrow indicates the sample inside a 5 mm tube). (b) A representative T2‐weighted proton MRI of an acellular scaffold. (c–e) Representative sodium MRI of chondrogenic constructs at day 7, day 14, and day 28 along with representative ROIs of the construct (bottom box) and reference media (top box). The average number of voxels in sodium images is 231 ± 22. Majumdar [23]. Reproduced with the permission of Springer. (See insert for color representation of this figure.)

(a)

–5

(b) Day 28

Day 28

Day 14

Day 14

Day 7

Day 7

–3 –1 1 3 5 FCD from sodium MRI (mM)

Mean FCD from sodium MRI (mM)

(c)

–3

–2 –1 FCD from GAG assay (mM)

0

y = 2.3849 x + 3 .1142 mM r = 0.7922 3 1 –1 –3 –5 –2.5

–1.5

– 0.5

0.5

Mean FCD from GAG assay (mM)

Figure 3.7  Calculated FCD for chondrogenic matrix made out of PLGA‐PuramatrixTM hydrogel hybrid scaffolds (n = 3) at day 7, day 14, and day 28 from (a) sodium MRI and (b) from GAG assay. The bar represents standard error in both cases. (c) The correlation between GAG‐derived FCD and sodium MRI‐derived FCD along with the regression equation and Pearson correlation coefficient. Majumdar [23]. Reproduced with the permission of Springer.

60

3  High Field Sodium MRS/MRI: Application to Cartilage Tissue Engineering

that the NaMRI is a sensitive tool for assessing proteoglycan generation in tissue‐ engineered cartilage. 3.4.2  Assessment of Tissue Anisotropy and Molecular Dynamics

The long and oriented collagen fibers provide the necessary structural and mechanical support for the load‐bearing viscoelastic functional properties of cartilage. As we discussed before, the average quadrupolar coupling ωQ is indicative of tissue anisotropy and has been widely used in assessing cartilage degradation in osteoarthritis and regeneration in tissue‐engineered cartilage [26, 27, 50]. The change in the values of T1, T2f, and T2s values that defines the molecular dynamics using the motional parameter ω0τc also signifies a progression in osteoarthritis [25]. For tissue‐engineered cartilage, the change in ω0τc signifies the progression of tissue maturity [26, 27]. We call ω0τc and ωQ “functional assessment parameters.” The extent of the deviation of these “functional assessment parameters” for any tissue‐engineered cartilage from native cartilage parameters can be termed as the lack of functionality (or lack of load‐bearing capabilities). In an experiment, we applied sodium TQC NMR spectroscopy to study the effect of tissue growth strategy on the dynamics and anisotropy in tissue‐ engineered cartilage [26]. Three different types of tissue‐engineered constructs were studied: chondrocytes pellets, chondrocytes seeded in alginate beads, and human marrow stromal cells seeded in ECM‐integrated biomimetic scaffolds. Figure  3.8a shows the TQC buildup curves in these three engineered cartilage tissues. It is clear from the figure that the buildup curves were strongly influenced by their environment. Table 3.2 shows the values of the fast (T2f ) and slow (T2s) spin–spin relaxation times along with the average quadrupolar coupling ωQ and the motional averaging parameter (ω0τc). From the table, we notice that the engineered cartilage tissues have a smaller ω0τc and ωQ when compared to natural human cartilage tissue. This is indicative of faster sodium ions’ motion and smaller tissue anisotropy in these engineered tissues compared to native cartilage tissue. The fast sodium ion motion could mean that there is significantly less binding between proteoglycans and sodium ions in engineered cartilage tissues. This may be because of the denser packing of proteoglycans and collagen in engineered cartilage. The reduced anisotropy may be due to random orientation and short collagen fibers owing to a short growth time in the engineered cartilage compared to the well‐organized long collagen fibers in natural cartilage. Figure 3.8b shows an example where the TQ spectra of engineered cartilage tissue at week 4 is sharper when compared to week 2 spectra, indicating the loss of some anisotropy during the growth period. These results also show that the choice of scaffold dictates the morphology of engineered cartilage tissues and influences the functional assessment parameters. The important question is as follows: Can this information be integrated into

3.4 ­Assessment of Tissue‐Engineered Cartilag

(a)

(b)

Triple-quantum signal intensity (a.u.)

7 6 Chondrocytes in alginate beads Chondrocyte pellets HMSCs in biomimitic scaffolds

5

Week 4

4 3

Week 2

2 1 0

0

20

40

60 80 τ (ms)

100

120

140 0.65 0.50 0.35 0.20 0.05 –0.10 –0.25 –0.40 –0.55 –0.7 ppm

Figure 3.8  (a) TQ signal intensity as a function of creation time for tissue‐engineered cartilage and their best fit with Equation 3.1 for 1‐day‐old engineered cartilage constructs. (b) The TQ signal of human marrow stromal cells (HMSCs) seeded in biomimetic scaffolds at week 2 and week 4. The week 4 spectrum is narrow compared to the week 2 spectrum, indicating faster motion or lower ω0τc at week 4. Reproduced with the permission from Kotecha et al. [26]. (See insert for color representation of this figure.)

our assessment criteria for the outcome of cartilage tissue engineering? Such information is not included in routine assessments of engineered cartilage tissues at present, but could become an important tool for creating a functional engineered cartilage. 3.4.3  Assessment of Osteochondral Tissue Engineering

Osteochondral tissue engineering seeks to create a functional tissue with tissue interface, where one side of the tissue is cartilage‐like and another side is bone‐ like (see Chapter 12 for more details on osteochondral tissue engineering). The assessment of osteochondral tissue is challenging with MR being the only noninvasive modality suitable to assess both cartilage and the bone side of the tissue. In our recent experiments using acellular bone and cartilage constructs developed for the same amount of time (4 weeks), we found that while osteogenic constructs had a strong TQ signal buildup, chondrogenic constructs had no TQ signal [27]. This showed higher tissue anisotropy or order in osteogenic constructs when compared to chondrogenic constructs that might have resulted from collagen I fibers in these bone constructs. Therefore, we predict that sodium TQMRI will be important for assessing osteochondroal tissue interfaces in the future.

61

18.7 ± 1.08 0.32 ± 0.03 2463 ± 798 8.42 ± 0.24

Human cartilage explants (n ≥ 3)

ωQ (Hz) T2s (ms)

—

T2f (ms)

5.45 ± 0.12 73 ± 35.6 12.0 ± 4.6

6.15 ± 0.16 —

ω0τc

a

—

ωQ (Hz)

2.42 ± 0.49

—

ω0τc

a

0.50 ± 0.03 628 ± 458

0.44 ± 0.04

6.5 ± 0.14

6.75 ± 0.2

No TQ

No TQ

No TQ

No TQ

No TQ

No TQ

No TQ

No TQ

0.29 ± 0.05 2601 ± 1357 7.84 ± 0.39 49 ± 1.33 15.5 ± 0.33 9.6 ± 13.2 1.26 ± 0.03

0.60 ± 0.04

a

0.51 ± 0.04 829 ± 459

T2f (ms)

Bovine chondrocytes in pellets (n ≥ 3)

Adapted with the permission from Kotecha et al. [26]. The values are given with standard parameter error of fitting. All experiments were performed using a 9.4 T (1H frequency = 400 MHz) Bruker Avance NMR spectrometer. As can be seen from the table, both the correlation time τc and the average quadrupolar coupling ωQ are smaller in engineered tissues when compared to those in native tissue. No TQ buildup signifies the isotropic environment for sodium ions. a  Value is not included in the table because the error in fitting exceeded the calculated parameter value many fold.

2.37 ± 0.43 4.65 ± 0.29 17.8 ± 1.0

41.0 ± 7.0

4.63 ± 0.47 16.4 ± 1.2

a

Week 4

2.2 ± 0.68

a

36.0 ± 10.6

6.69 ± 0.58 14.8 ± 1.2

Week 3

6.30 ± 0.71 15.2 ± 0.7

a

51.1 ± 11.1

1.4 ± 0.37

55.0 ± 14.8 1.65 ± 0.55

Week 2

4.98 ± 0.26 16.3 ± 1.0

a

T2s (ms)

Bovine chondrocytes in alginate beads (n ≥ 3)

211 ± 190

ω0τc

Week 1

2.09 ± 0.3

ωQ (Hz)

44.5 ± 5.6

T2f (ms)

Week 0

T2s (ms)

Human marrow stromal cells in biomimetic scaffolds (n ≥ 3)

Table 3.2  The average quadrupolar coupling ωQ and motional parameter (ω0τc) for scaffold‐free and scaffold‐based tissue‐engineered cartilage for 4 weeks of culture time and native cartilage.

3.5 ­Sodium Biomarkers for Engineered Tissue Assessmen

3.5 ­Sodium Biomarkers for Engineered Tissue Assessment Based on our previous discussion, the NaMRS/NaMRI assessment map using three biomarkers in tissue‐engineered cartilage is shown in Figure 3.9. However, these parameters are not restricted to only tissue‐engineered cartilage and can be applied in the assessment of other engineered tissues, where anisotropy and sodium concentration play an important role in tissue function. In the following text, we summarize and discuss the role of each parameter in defining the specific properties of engineered cartilage and any other engineered tissue in general. 3.5.1  Engineered Tissue Sodium Concentration (ETSC)

The TSC is an important biomarker for tissue pathology. It also provides an estimate of tissue growth for tissue‐engineered cartilage. Engineered TSC (ETSC), calculated using the properties of scaffold and cell density, can be used to define the sodium concentration in engineered tissues. ETSC has been shown to correlate with the amount of proteoglycans in PLGA‐PuramatrixTM‐ based engineered cartilage [23]. There is a possibility of using ETSC for tissue growth estimates in other tissues, such as brain‐ and muscle‐engineered tissues. Engineered tissues often contain a scaffold in addition to cells and a growing ECM. These volume fractions should be taken into account when calculating ETSC in engineered tissues.

ETSC (amount of proteoglycans)

Sodium MRS/MRI in tissue engineered cartilage τc (macromolecular composition)

ωo (collagen fiber orientation)

Figure 3.9  Sodium MR biomarkers for the assessment of cartilage tissue engineering.

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3  High Field Sodium MRS/MRI: Application to Cartilage Tissue Engineering

3.5.2  Average Quadrupolar Coupling (ωQ)

The average quadrupolar coupling arises from nonaveraged EFGs which in turn arise from sodium ions in anisotropic tissues. Therefore, ωQ is a direct measure of tissue anisotropy. It is a valuable biomarker, where the tissue ­anisotropy is important for its functionality, such as cartilage, muscle, cardiovascular, and bone 3.5.3  Motional Averaging Parameter (ω0τc)

The motion of sodium ions depends on its surroundings and the binding of sodium ions with various extracellular macromolecules. The slow motion that defines the average quadrupolar coupling is responsible for the generation of the TQC signal. Therefore, the motional averaging parameter ω0τc is an important biomarker of tissue maturity. As a tissue matures and its complexity increases, ω0τc increases to reflect this trend. It has been used to define the motion of sodium ions in engineered cartilage. In those instances, it was ­further found that sodium ions exhibit faster motion in engineered cartilage signifying the lack of complexity and maturity in the engineered tissues studied [26]. We expect that this will also provide an indication of tissue maturity in other directional tissues, such as muscle and bone

3.6 ­Future Directions The low SNR of NaMRI when compared to the traditional water proton MRI is challenging. This fact has driven recent research to improve SNR. These include improvements in pulse sequence design, both for TSC quantification and the TQC signal [28, 51–53]. Cross‐polarization from protons to low gamma nuclei, such as sodium, has been extensively applied in NMR research [54–58] and may be applied to signal enhancement for the in vitro assessment of engineered tissues, where the specific absorption rate (SAR) is not a concern. However, its application in animal and human research is questionable. Recent developments in the dynamic nuclear polarization (DNP) of quadrupolar nuclei are interesting and may have applications in the field of NaMRI assessment of engineered tissues [58]. Biomaterials are important structure components in tissue engineering and further research is needed to determine how they affect the sodium MRI assessment of engineered tissues (see Chapter 2).

3.7 ­Summary NaMRI offers distinct advantages over the standard water proton‐based MRI. The changes in TSC and sodium relaxation times are important biomarkers of tissue growth and maturity, especially in the case of cartilage tissue engineering.

 ­Reference

The calculated FCD derived from TSCs has been shown to correlate with FCD derived from GAG after taking into account the volume fractions occupied by scaffold and cells. The average quadrupolar coupling ωQ and correlation time τc derived using sodium TQC in tissue‐engineered cartilage revealed that engineered tissues have lower anisotropy and faster sodium ion motion when compared to natural tissue. If incorporated in tissue assessment on a regular basis, these results may lead to the development of functional tissue‐engineered cartilage in the future. It is expected that NaMRI will play a vital role in cartilage tissue engineering assessments in the future. Further investigations are needed to gauge the role of NaMRI in the assessment of other engineered tissues.

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bone and engineered cartilage. Conf Proc IEEE Eng Med Biol Soc. 2014;2014:3929–3932. Tanase C, Boada FE. Triple‐quantum‐filtered imaging of sodium in presence of B(0) inhomogeneities. J Magn Reson. 2005;174(2):270–278. Parish TB, Fieno DS, Fitzgerald SW, Judd RM. Theoretical basis for sodium and potassium MRI of the human heart at 1.5 T. Magn Reson Med. 1997;38(4):653–661. Borthakur A, Hancu I, Boada FE, Shen GX, Shapiro EM, Reddy R. In vivo triple quantum filtered twisted projection sodium MRI of human articular cartilage. J Magn Reson. 1999;141(2):286–290. Navon G, Shinar H, Eliav U, Seo Y. Multiquantum filters and order in tissues. NMR Biomed. 2001;14(2):112–132. Chandrakumar N. 1D Double Quantum Filter NMR Studies. In: Graham AW, editor. Annual Reports on NMR Spectroscopy. Volume 67: Academic Press, London/New York; 2009, pp. 265–329. Matthies C, Nagel AM, Schad LR, Bachert P. Reduction of B(0) inhomogeneity effects in triple‐quantum‐filtered sodium imaging. J Magn Reson. 2010;202(2):239–244. Fiege DP, Romanzetti S, Tse DH, Brenner D, Celik A, Felder J, et al. B0 insensitive multiple‐quantum resolved sodium imaging using a phase‐rotation scheme. J Magn Reson. 2013;228:32–36. Centers for Disease Control and Prevention (CDC). Prevalence and most common causes of disability among adults—United States, 2005. MMWR Morb Mortal Wkly Rep. 2009;58(16):421–426. Hwang NS, Varghese S, Elisseeff J. Cartilage tissue engineering: directed differentiation of embryonic stem cells in three‐dimensional hydrogel culture. Methods Mol Biol. 2007;407:351–373. Buckwalter JA, Mankin HJ. Articular cartilage: tissue design and chondrocyte‐ matrix interactions. Instr Course Lect. 1998;47:477–486. Lesperance LM, Gray ML, Burstein D. Determination of fixed charge density in cartilage using nuclear magnetic resonance. J Orthop Res. 1992;10(1): 1–13. Mow VC, Kuei SC, Lai WM, Armstrong CG. Biphasic creep and stress relaxation of articular cartilage in compression? Theory and experiments. J Biomech Eng. 1980;102(1):73–84. Lai WM, Hou JS, Mow VC. A triphasic theory for the swelling and deformation behaviors of articular cartilage. J Biomech Eng. 1991;113(3):245–258. Zbyn S, Stelzeneder D, Welsch GH, Negrin LL, Juras V, Mayerhoefer ME, et al. Evaluation of native hyaline cartilage and repair tissue after two cartilage repair surgery techniques with 23Na MR imaging at 7 T: initial experience. Osteoarthritis Cartilage. 2012;20(8):837–845.

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4 SPIO‐Labeled Cellular MRI in Tissue Engineering: A Case Study in Growing Valvular Tissues Elnaz Pour Issa1 and Sharan Ramaswamy 1,* 1

Department of Biomedical Engineering, Florida International University, Miami, FL, USA * Corresponding author email: [email protected]

4.1 ­Setting the Stage: A Clinical Problem Requiring a Tissue Engineering Solution The incidence of congenital heart valve disease varies anywhere from 5 to about 12 per 1000 live births [1]. However, the effective treatment of congenital heart valve anomalies is very problematic. Many of these patients are newborns and the replacement of prosthetic valve approaches that routinely are performed in adults are simply not feasible in children due to practical limitations in sizing, somatic growth, and effectiveness. Thus, without timely intervention, the incidence of morbidity and death is extremely high. Currently, multiple operations are often required to accommodate the growth of the young patients [2]. Developing tissue‐engineered heart valves (TEHVs) as a permanent replacement for defective and diseased valves has the potential to revolutionize treatment of heart valve diseases. It will especially benefit the young patients who currently have no viable option. A primary step in designing TEHVs would be selecting the cell source(s) appropriately. Bone marrow stem cells (BMSCs), vascular cells (VCs) such as smooth muscle cells (SMCs) and endothelial cells (ECs), periodontal ligament‐derived stem cells (PDLSCs), as well as other progenitor cell sources have been considered in heart valve tissue engineering [3–6]. Previously, investigations in the Ovine model were conducted using BMSCs, which showed that engineered heart valves can exhibit biochemical characteristics and maintain robust functionality similar to the native valves [7]. These investigations were done in the pulmonary flow environment. However, substantial work is still required to identify leaflet Magnetic Resonance Imaging in Tissue Engineering, First Edition. Edited by Mrignayani Kotecha, Richard L. Magin, and Jeremy J. Mao. © 2017 John Wiley & Sons, Inc. Published 2017 by John Wiley & Sons, Inc.

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features for the purposes of establishing detailed functional and durability evaluation, particularly for aortic and mitral valve replacement. It is of significance to monitor cell patterns during the operation of TEHVs which will serve to optimize construct design features during tissue remodeling events. One useful approach is to label cells with superparamagnetic iron oxide (SPIO) particles and to track them longitudinally with MRI. Intrinsically, the labeling materials should not cause any harm to the cells, and signal from these contrast agents need to be adequately detected [8, 9]. These types of experiments have been performed at the in vitro level but further studies within in vivo and clinical domains are very much needed. Nonetheless, both in vitro and in vivo, MRI has thus far been utilized to screen cellular migration within the scaffold and the surrounding tissues [10]. In this chapter, we will discuss some concepts of SPIO‐based MRI in heart valve tissue engineering studies.

4.2 ­SPIO Labeling of Cells 4.2.1 Ferumoxides

A robust approach to noninvasive cell tracking would be in the use of SPIO particles to longitudinally track cellular migration, and visualize them as spatial regions of signal voids or hypointensity using MRI. Effective and repetitive cell rinsing, washing, and filtering steps need to be performed during protocol development stages to ensure that iron oxide particles are contained only within the cell. Evidence of intracellular iron can be validated using Prussian blue histological staining. Ferumoxide is fairly inert when present within the cytoplasm of most mammalian cell types. The cells labeled with SPIO particles can be monitored noninvasively using MRI by primarily, transverse relaxation time (T2) contrast and even more sensitively via the “effective” transverse relaxation time parameter (T2*), owing to its ability to capture magnetic field inhomogeneities induced by the presence of iron compounds [8, 9, 11]. This visualization is usually required from the very early stages of tissue‐engineered construct development thereby requiring longitudinal assessment of cell migratory and distribution events within the scaffold. For instance, MRI has been used for the detection of SPIO‐labeled chondrocytes in tissue‐engineered cartilage constructs such as a hollow‐fiber bioreactor (HFBR) system and a photopolymerizable hydrogel. Chondrocytes were labeled with SPIO particles to evaluate ferumoxide cellular uptake, and extracellular matrix (ECM) production. The goal in such experiments was to demonstrate that SPIO‐labeled chondrocytes were able to robustly secrete chondrogenic ECM, and yet could be longitudinally tracked noninvasively using MRI [12–15]. These studies follow from a number of investigations examining liver pathologies with ferumoxide contrast, whereby there are significant differences in T2 and T2* relaxation between the

4.2 ­SPIO Labeling of Cell

Figure 4.1  Polymer microsphere: both the iron oxide and the red fluorescent tag are centrally encapsulated in the core region of the polymer microsphere. Hence, the star‐shaped icon as seen in the figure provides indirect evidence of intracellular SPIO uptake. In other words, fluorescence microscopy can be used to validate the cellular MRI findings. Martinez et al. [7]. Reproduced with the permission of SAGE Publications.

Polymer

Fluorescent tag Iron oxide

normal tissue and the pathological lesions [16]. A large challenge in tissue engineering‐based cellular MRI studies is the validation of the images with a goal standard technique such as histology or a more accepted imaging modality. For example, commercially available microparticles (e.g., from Bang’s Laboratories, Inc., Fishers, IN) consist of a core region containing iron oxide particles and a red fluorescent tag (Figure 4.1). Cells labeled with Bang’s particles can be verified for effective iron oxide uptake using fluorescence microscopy. A brief description of different types of SPIO nanoparticles is summarized in Table 4.1. ●●

●●

Feridex (Berlex Laboratories, Montville, NJ): these first generation particles were very popular in a number of research studies involving cellular, ferumoxide‐based MRI; however, this product is currently discontinued. Feridex has been used extensively in liver tissue imaging. Feridex can be described as a suspension consisting of SPIO nanoparticles coated with a thin layer of dextran [18–21]. Second and third generation SPIO nanoparticles: these particles are mixed with polymer layers and stay monodispersed in solution [22, 23]. In order to image SPIO nanoparticles, T2*‐weighted images above 0.5 T are generally considered [21, 24]. By using greater signal averaging, the image quality improves with enhancement of signal‐to‐noise ratios [19, 20]. In summary, the utility of iron oxide nanoparticles for traditional MRI cell tracking purposes has previously been extensively reported by Arbab et al. [9, 10]. These approaches have garnered interest for tissue engineering applications in recent times.

4.2.2  Transfection Agents

Transfection agents (TAs) are used along with SPIO nanoparticles to increase the cellular iron oxide uptake. TAs are used in very low concentrations because of their cytotoxicity. However, TAs substantially increase the efficiency of cellular labeling [18, 25]. Where SPIO‐based cellular MRI is concerned, there are three commonly used TAs, namely, protamine sulfate (PS), lipofectamine, and poly‐l‐lysine (PLL). Lipofectamine is a very efficient TA but can be somewhat

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Table 4.1  SPIO nanoparticles approved by the Food and Drug Administration or under clinical trials from pharmaceutical companies. Company

Brand

Size (nm)

Coating

Guerbet, AMAG Pharm, Inc.

Ferumoxides

120–180

Dextran T10

AMI‐25

15–30

Dextran T10, T1

60

Carboxydextran

21

Carboxydextran

30

Polyglucose sorbitol carboxymethyl ether

20

Pegylated starch

7

Citrate

Feridex/Endorem Ferumoxtran‐10 AMI‐227 Combidex/Sinerem Bayer Schering Pharma AG

Ferucarbotran SHU 555A Resovist 60 Carboxydextran SHU 555C Supravist Ferumoxytol Code 7228

GE healthcare

Feruglose NC‐100150 Clariscan

Ferropharm

VSOP‐C184

Wang et al. [17]. Reproduced with the permission of Ivyspring International Publisher

toxic depending on the cell type being labeled. PS is a drug that binds to heparin to form a constant ion pair without having anticoagulant activity. Not surprisingly, it has been shown to be safe to use with cells as it is used routinely in tissue culture, gene transfer, and protein purification. However, there is a slight concern regarding propensity for cardiovascular complications such as hypertension [26, 27]. The third kind of TA is PLL. The precursor amino acid lysine has two different amino groups, one at the α‐carbon and one at the ε‐carbon. Either can be polymerized, resulting in α‐polylysine or ε‐polylysine. The ε‐polylysine acts as an antibacterial and antimicrobial agent. The α‐polylysine is commonly used to coat tissue cultureware as an attachment factor to improve cell adherence. PLL has a light yellow appearance [28] and is also commonly used for enhancing cellular SPIO‐uptake.

4.2 ­SPIO Labeling of Cell

4.2.3  Labeling Protocols

In cell‐based therapy approaches, it is important to consider tracking studies to determine the fate of the transplanted cells in in vivo environments. Contrast labeling using SPIO particles can permit considerable distinction of the cells from their surroundings in MRI images, even in a complex in vivo setting. Various labeling protocols involving a variety of combinations of ferumoxide particles with TAs have been used to magnetically label the cells. Dextran‐ coated iron oxide particles [29, 30], liposome‐encapsulated iron oxide particles [31], and lectin‐mediated uptake [32] were reported in various studies. Martinez et  al. used Bangs particles to label the VCs (human pulmonary artery‐derived SMCs and ECs) [7]. They labeled human vascular SMCs and ECs, and BMSCs with SPIO microparticles and visualized them using MRI under steady flow [33]. It was observed that VCs can be visualized and tracked very well after being labeled with SPIO microparticles (200 mg/ml) for 48 h. In addition to the particle properties, the specifics concerning the cell‐labeling protocol have a significant effect on labeling efficiency. SPIO‐labeling dose and incubation time influence the efficiency of ferumoxide incorporation, distribution, retention, and ultimately, cell behavior. Van Tiel et al. showed that for the human umbilical vein endothelial cells (HUVECs), the optimal labeling protocol with an incubation time of 24 h resulted in 12 pg iron oxide uptake/cell and an efficiency of 9.6% [34]. At shorter incubation times, (e.g., 4 h) particles stick to the outside of the cell instead of being taken up. It is worthwhile noting that the different cell types react differently to identical labeling protocols. Therefore, the optimal labeling protocol depends heavily on the cell type and thus, every SPIO‐based cellular MRI study needs to begin with an “efficiency of SPIO cell labeling” investigation, in terms of maintaining cell viability, proliferation, and phenotype (compared to unlabeled controls), while simultaneously allowing for robust MRI contrast. It was found that even when similar labeling protocols were used, various cell types showed different cell responses [35, 36]. SPIO nanoparticles were used to label a murine cell (C2C12 myoblasts); in order to augment SPIO uptake, lipofectamine was used during ferumoxide incubation for 15 min [37]. It was found that the concentration of 20 mg Fe/ml and 0.5 mg/ml lipofectamine and the incubation time of 12 h demonstrated very good cellular uptake of the SPIO particles. Suzuki et al. conducted a precise assessment of PLL, PS, and electroporation (ELP) usage to enhance SPIO endocytosis [37]. The cellular viability, apoptosis, proliferation, and cardiac differentiation of magnetically labeled mouse embryonic stem cells (mESCs) were evaluated. To label the mESCs, ferumoxide particles with the size of approximately 80–150 nm were used. Although no significant differences between labeled and unlabeled groups were observed in the case of viability, apoptosis, and proliferation, it was found that ELP influenced cardiac differentiation adversely, while iron uptake was most effective using PS.

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4.3 ­Applications 4.3.1  Traditional Usage of SPIO‐Labeled Cellular MRI

It has been several years since SPIO nanoparticles have been used as contrast agents for diagnostic imaging applications. More recently, labeling stem cells with SPIO nanoparticles is proving to be a useful technique in cell therapy [38–40]. While other tracking modalities may permit greater spatial resolution (e.g., bioluminescence imaging), MRI is currently the only noninvasive and nondestructive technique that can track and assess cell fate in deep tissues and organs. Direct labeling of stem cells with SPIO particles is relatively safe, inexpensive, and quick. With stem cells, the iron oxide nanoparticles have been found to be safe for the cells [39] and exogenous ferumoxides are washed out of the system. Clinically relevant protocols for cell labeling, including stem cell therapy, have been discussed in detail by Frank et al. [41, 42]. Instead of utilizing TAs, magnetoelectroporation uses low‐pulsed voltages, that is, 130 V for cell therapeutic purposes [43]. This method has been found to be very applicable in clinical trials as millions of cells can be labeled quickly and returned to the patient. A study was conducted recently on embryonic stem cells using magnetoelectroporation versus TAs, and the results showed that magnetofection, that is, using TAs, had greater iron uptake as opposed to magnetoelectroporation [37]. Protocols developed that were either magnetofection‐based or magnetoelectroporation‐based that were used to label cells with SPIO for cell therapy purposes have led to similar approaches for SPIO‐based cellular MRI in tissue engineering. 4.3.2  SPIO‐Labeled Cellular MRI in Tissue Engineering

Cell labeling with SPIO particles for the monitoring of engineered tissue constructs using MRI is a relatively recent area of research. As part of this process, it is important to capture the migration patterns of the cells within the scaffold, as well as to and from the scaffold region. Terrovitis et  al. conducted early work in the tissue engineering area and demonstrated robust SPIO labeling of BMSCs [8]. Subsequently in an in vivo study conducted by Ko et  al. in the mouse model, SPIO‐labeled BMSCs encapsulated within a gelatin sponge scaffold were successfully visualized and tracked via MRI after sponge implantation [44]. The constructs were found to be safe to use and nontoxic to the animals. Harrington et al. used cellular MRI to track SPIO‐labeled cells in the vascular tissue‐engineered graft application via a series of T2‐weighted images, and they were able to effectively monitor graft development in vivo [45]. By quantification of T2 and observing longitudinal increases in T2, the authors demonstrated longitudinal loss of contrast and hence loss of cells from the scaffold region.

4.4 ­Case Study: SPIO‐Labeled Cellular MRI for Heart Valve Tissue Engineerin

A summary of key studies involving SPIO particles for tissue engineering are as follows: feasibility of injectable biodegradable polymer microspheres containing SPIO nanoparticles for drug delivery demonstrated localized identification of the spheres initially at the site of injection and subsequently were successfully tracked in vivo by MRI in the mouse model [46]. These studies demonstrated the applicability of using microspheres as the vehicle for not only drugs but in theory, also for targeted delivery of progenitor or stem cells to an injured/diseased site. Iron oxides were used as a means to track mesenchymal stem cells in a large animal model (Ovine) of tendon injury [47]. The purpose of the study was to determine an SPIO‐based tracking agent for tendonitis. The cells were successfully tracked using MRI for 7 days following injection and facilitated noninvasive monitoring of tendon regeneration. Elsewhere, ultra‐small superparamagnetic iron oxide (USPIO) nanoparticles were used to label different collagen scaffold m ­ aterials [48]. The ability to track MRI signal intensities permitted the assessment of degradation of these scaffolds, which was critical in being able to tailor the rate of material degradation to the rate of tissue regeneration. These findings illustrate the importance of SPIO labeling and subsequent MRI monitoring for enabling image‐guided optimization of a given tissue engineering treatment. Ramaswamy et al. performed extensive MRI studies to monitor tissue‐engineered cartilage that were grown in fibrous and gel‐based scaffold environments [49]. The investigations showed that the chondrogenic phenotype was well preserved following labeling with the robust expression of type II collagen and aggrecan, and the presence of de novo engineered tissues rich in additional cartilaginous ECM components. Furthermore, the authors validated the MRI‐identified spatial locations of cells and new tissue growth with corresponding histological sections. This was possible with the use of a high‐field MRI instrument (9.4 T) combined with multiple signal averaging, which resulted in high signal‐to‐noise in relatively thin image slices (200 µm), which were subsequently found to be accurate via validation against corresponding histological slices (10–20 µm).

4.4 ­Case Study: SPIO‐Labeled Cellular MRI for Heart Valve Tissue Engineering 4.4.1  Experimental Design

Treatment of congenital valve disease in pediatric patients may require valve repair and/or replacement. Yet, no available prosthetic valve offers the possibility for somatic growth. Bioprosthetic valves offer limited durability because of accelerated calcification. Mechanical valves offer excellent durability but a requirement of concomitant anticoagulant use makes it a limiting option for

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some patients. Both types of prosthetic valves have practical sizing limitations, particularly for neonates born with critical valve defects. TEHVs offer the promise of a one‐time permanent treatment of critical valve disease with the added growth benefit of the valve with the patient, thereby omitting the need for multiple, complex reoperations. Measured amounts of success have been reported in clinical [50] and in vivo TEHV studies [5]. Ultimately, implanted TEHVs undergo structural and functional tissue remodeling even though there are many unknowns regarding its long‐term functionality and durability [51]. Identification of the nature of the remodeling process including cell fate within the TEHV constructs can be studied using SPIO‐based MRI. In our laboratories, we have previously assessed native VC migration patterns in both static and perfusion‐based experiments [7, 39]. The pulmonary artery consists of vascular endothelial cells (VECs) and vascular smooth muscle cells (VSMCs). SPIO particles were used to label these cells to track their fate, visualizing them as regions of low signal or hypointensity, using cellular MRI methods [8, 9, 11, 44]. The cellular uptake of SPIO particles was confirmed via Prussian blue histological staining (Figure  4.2). Next, in vitro dynamic experiments were conducted in an MRI compatible, custom‐designed bioreactor system (Figure  4.3). The results demonstrated that flow environments altered cellular migrations patterns within the scaffold region and in comparison to static controls led to a more spread‐out cellular distribution within the scaffold. These findings contributed to enhanced stem cell‐derived valvular tissue growth and phenotype when a flow component was incorporated in the culture protocol, in comparison to static conditions [53]. 4.4.2  Potential Approaches—In Vitro

As alluded to earlier, for in vitro heart valve tissue engineering experiments, bioreactors are commonly utilized. Broadly, bioreactors are devices that can provide physical or mechanical stimulation to growing cells and tissues, and at the same time facilitate the intrinsic requirements present in static tissue growth (media biochemical properties, adequate gas exchange, and temperature control). Prior to implanting the engineered heart valves, bioreactors could also be used as preclinical tools for mechanical stimulation of engineered tissue, which has been shown to enhance cell and tissue growth [5, 7, 49, 53–55]. While the overall goal is to be able to image cells in the native in vivo heart valve environment, in vitro approaches provide insights on the specific effects of certain variables (e.g., fluid‐induced shear stress) in a highly controlled fashion. Superior valve tissues are also obtainable in vitro by regulating cell biological response through specific sets of physiologically relevant mechanical modes of stimuli (e.g., flow, stretch, and flexure (FSF)) [54, 56, 57]. For example, Hoerstrup et al. grew valve constructs in a bioreactor system under controlled pressure and flow conditions [54]. As a result, the engineered tissues were found

4.4 ­Case Study: SPIO‐Labeled Cellular MRI for Heart Valve Tissue Engineerin

(a)

100 µm

100 µm

50 µm

50 µm

(b)

(c)

20 µm

20 µm

Figure 4.2  (a) Morphology of human VSMCs (left) and VECs (right). (b) Prussian blue histological stain demonstrating the endosomal uptake of SPIO microparticles by the human VSMCs (left) and VECs (right). (c) Higher magnification (×400) view of positive dark staining in single VSMC (left) and VECs (right). Ramaswamy et al. [39]. Reproduced with the permission of John Wiley & sons.

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(a)

Axial rail 27 mm 13 mm

24 mm

80 mm

220 mm

(b)

(c) Top H

A E

C

F

Right

Left c=4.3 mm

G

D

Y Z

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Figure 4.3  (a) Cutout longitudinal section of a flow, stretch, and flexure (FSF) bioreactor showing several key dimensions. (b) Closeup view of key components in one of the bioreactor’s conditioning chamber. (c) Cross section showing orientation and the placement of a specimen when flat in the flow tube, which were placed off‐center by a distance of 4.3 mm. When flexed, the specimen will protrude into the center of the tube, moving in the left to right direction. Legend is as follows: A. Polyetherimide (ULTEM) chamber providing an excellent MRI susceptibility match to media and tissues, B. U‐shaped fluid enclosure, C. Sliding sample holder, D. Outer tube, E. Ring, F. Moving post, G. Fixed post, H. Injection port. Ramaswamy et al. [52]. Reproduced with the permission of The American Society of Mechanical Engineers.

to be more resilient and higher in mechanical strength, which is important for the valve application, owing to its placement in an intense hemodynamic environment. Our most recent experience in this field unequivocally shows that we were able to promote heterogeneous differentiation of BMSCs to endothelial and activated interstitial cell phenotypes under a physiologically relevant combination of cyclic flexure (1 Hz) and steady flow (mean shear stress on ventricular side was 4.73 dynes/cm2) [57]. Furthermore, the cells that differentiated to ECs were found on the surface of the constructs and largely absent from the interstitium, whereas the stem cells that had differentiated into activated

4.4 ­Case Study: SPIO‐Labeled Cellular MRI for Heart Valve Tissue Engineerin

Figure 4.4  Bioreactor flow chamber containing SPIO‐labeled cells for MRI studies. Martinez et al. [7]. Reproduced with the permission of SAGE Publications.

Bioreactor chamber Scaffolds

Inlet

Outlet

Cell culture media

SPIO-labeled cells

Flow direction steady flow rate of 850 ml/min (~5–6 dynes/cm2 shear stress)

interstitial cells were found deep within the tissues and were absent from the surfaces. These findings demonstrate that the mechanical conditions utilized not only support the valvular phenotype but also distributed the cells in a manner depicting the native valve. In a similar framework, our lab has attempted to build bioreactors out of nonmetallic and nonmagnetic parts which can be imaged using MRI [52]. Subsequently, SPIO‐labeled cells can be loaded into the bioreactor chamber for cell tracking studies (Figure 4.4). Typically, mechanical stress application is temporarily paused during the imaging phase and the bioreactor is returned promptly to the incubator for continuation of culture, at which point the mechanical stress application is resumed. Thus, cellular MRI can occur longitudinally with intermittent breaks for the imaging process (in the order of a few hours) at selected temporal points during the dynamic tissue culture process (in the order of a few weeks). Our early results in the context of heart valves indicate that steady‐flow mechanical conditioning enhances cell migration processes as well as promotes uniformity in cell distribution across and through the thickness of the scaffold, which results in a more even distribution of engineered valvular ECM [7]. 4.4.3  Potential Approaches—In Vivo

Naturally, the visualization and monitoring of the cells within TEHVs would be ideal from the very beginning of the process, that is, during the in vitro culturing stage and then continued, even after implantation [3, 58, 59]. All indicators suggest that SPIO as an MRI cell tracking agent is safe and does not alter the cell activity, but this still has to be assessed if the cell type is new and has not undergone a cell cytotoxicity evaluation. After this requirement has been satisfied, it is critical that the SPIO‐labeling remains intracellularly contained (but does not penetrate the cell nucleus) for the specified period of time that involves longitudinal monitoring and visualization via MRI; this could be in the

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order of weeks to several months. This visualization process includes the migration of cells within and outside of a scaffold region, as well as the potential migration of native cells from surrounding vascular tissue regions into the engineered heart valve tissues. The challenges associated with imaging a native valve via MRI will carry over to the TEHV, and principally this may be related to the valve motion which requires appropriate gating. In particular, MRI of the valves on the left side of the heart such as the mitral valve can be especially difficult as there are complications associated with electrocardiogram (ECG) gating and can be further aggravated if comorbidities such as atrial fibrillation is present [60]. In the case of the aortic valve, the effective orifice area (EOA) is often underestimated in cases of stenosis. For these reasons, echocardiographic procedures remain the gold standard for valve imaging and will be an essential component to any TEHV in vivo investigation. On the other hand, for TEHV cell tracking measures, MRI stands alone and can fortunately also be leveraged to provide useful diagnostic information, in addition to standard echocardiographic assessment. For example, MRI is already routinely used for the detection of pulmonary valve regurgitation and its excellent quantification of trasvalvular peak velocities. Coincidently, the focus area that has come closest to clinical usage of TEHVs is in the replacement of critically diseased pulmonary valves [50]. Typically, a heart valve MRI protocol comprises ECG gating with breath‐ hold acquisitions using a steady‐state free precession imaging sequence (SSFP), that is, a low‐flip‐angle gradient echo sequence with a short repetition time (TR) compared to T2. Subsequently, the velocity‐encoded imaging technique (or phase contrast imaging) comprising contiguous short‐axis slices that are built into a generated movie file can permit quantification of hemodynamic parameters, namely, velocity/forward flow volume and regurgitant volume. An additional MRI sequence with the intent of enhancing T2 and T2* contrast can be subsequently employed for SPIO‐based, cellular MRI investigations in TEHVs; a proposed protocol could be as follows: one of three TEHV leaflets can be seeded with SPIO‐labeled stem cells (e.g., BMSCs), while the other two leaflets can be seeded with unlabeled vascular ECs and vascular myofibroblasts (VMFs) (Figure 4.5). This process can be repeated by SPIO labeling of the ECs and subsequently the VMFs while keeping the BMSCs unlabeled. In vivo experiments following institutional animal care and use (IACUC) approval (e.g., in the Ovine model, which is popular for heart valve studies) can be run for a period of 10 weeks with MRI scans being performed on a weekly basis to obtain T2 measurements. T2 is an indicator of molecular motion and is hence longer (slower) in structures that have higher water content. Due to the magnetic susceptibility of SPIOs, the magnitude of T2 will decrease significantly which will enable distinct contrast differences between structures containing iron oxides and those that do not. The structures that contain SPIO particles will create artifacts that are large enough to be visualized clearly with MRI. T2  will be calculated from the reciprocal of the exponent of a plot of signal

4.5 ­Conclusions and Future Outloo

BMSC EC VMF

Figure 4.5  Experimental design for tri‐leaflet scaffold assembled constructs for longitudinal in vivo cellular tracking MRI studies of the implanted TEHVs. Serial SPIO labeling of one of the three cell types will occur to assess migratory patterns of each. MRI results can then be compared and validated against terminal histological endpoints after animal sacrifice.

intensity against echo time, according to the equation S S0 e t /T2 , where S stands for the mean, spin echo signal intensity at time t, and S0 is the signal intensity immediately following the radiofrequency pulse (time t = 0 ms). BMSC migration will be quantified in terms of the size of the areas of hypointense (dark) regions on magnetic resonance images. Longitudinally speaking, a decrease in area over time would indicate loss of BMSCs from the scaffold leaflet geometry and conversely, an increase would indicate cell aggregation within the scaffold and in situ proliferation of the cells. However, the signal intensity of the cells may potentially decrease as proliferation occurs. To get a sense of the range of signal intensities over cell expansion, one could include a set of cell culture experiments, wherein the MRI signal intensity of SPIO‐labeled BMSCs with increasing passage numbers can be measured. This will establish the timeframe in which one can visualize the BMSCs using the aforementioned SPIO‐ based cellular MRI methods.

4.5 ­Conclusions and Future Outlook As highlighted in this review, SPIO‐based cellular MRI for tissue engineering applications is gaining rapid popularity. These studies derive from earlier work focusing on cell therapeutic investigations and follow similar approaches, which include (i) utilization of TAs to enhance SPIO cellular uptake, (ii) assessment of progenitor cell cytotoxicity, proliferation, and differentiation capacity after ferumoxide uptake, (iii) assessment of time course of visualization and longitudinal loss of contrast, that is, efficiency of SPIO cellular uptake, in the context of MRI detection, and finally, (iv) actual benefits in noninvasive monitoring of tissue remodeling with potential for quantification of biophysical parameters (e.g., decreasing T2 as an indicator of de novo tissue growth) [61]. Acute evaluation generally shows SPIO usage to be safe for cells but because of unknown longer term risks, it is unlikely that SPIOs will be incorporated as

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part of the engineered tissue product’s routine clinical regimen. Instead, its incorporation will likely occur after initial establishment of feasibility of a ­tissue engineering strategy and will thus serve as a critical preclinical reagent, so that cellular fate within scaffold environments can be determined over several weeks to months. This monitoring will be helpful from initial in vitro preparation to eventual in vivo translation, and finally for correlation with histological terminal endpoints. After all, the purpose of any in vitro cell‐to‐scaffold seeding process is to ensure that the cells are maintained in their intended in vivo environment so as to permit controlled matrix integration, growth and remodeling, and to develop into permanent functional tissues. Recent SPIO‐based MRI studies in tissue engineering appear promising for the monitoring of both cell fate within scaffold structures and the degradation of the scaffold. In particular, these imaging studies have served to identify problems such as loss of cells after in vivo implantation thereby permitting corrections in the tissue engineering protocol. For cell visualization and tracking studies in deep tissues and organs needing such noninvasive and nondestructive assessment, MRI may be the only realistic option despite its current shortcomings. Such studies have already become an integral part of musculoskeletal soft tissue and cardiovascular tissue engineering investigations. Our own work highlighted in this chapter focusing on the development of engineered heart valve tissues is a testament to this observation. Tissue engineering technologies in these applications are likely to undergo extensive evaluation at the stage of large‐scale in vivo assessment, that is, a precursor to clinical trials, over the next decade. These studies will require robust SPIOs, TAs, and supporting MRI hardware/ software accommodations (e.g., specialized surface coils and hypointensity‐ tracking image analyses tools); therefore, the biomedical industry is also likely to see heightened activity in these areas as the tissue engineering sciences go through this period of rapid advancement in the foreseeable future.

­Acknowledgment The authors gratefully acknowledge the Center for Excellence in Writing at Florida International University for proofreading assistance to ensure grammatical and typographical accuracy of this book chapter.

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5 Magnetic Resonance Elastography Applications in Tissue Engineering Shadi F. Othman1,* and Richard L. Magin2 1

School of Engineering and Computer Science, University of the Pacific, Stockton, CA, USA Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA * Corresponding author email: [email protected]

2

5.1 ­Introduction Conventional tissue engineering (TE) strategies are used to treat several dis­ ease conditions. To be successful, TE relies on the principles of biology, cell transplantation, material science, and engineering to combine the appropriate cells, growth factors, and biomaterial scaffolds to create a functional tissue. However, current methods are ineffective and costly. To put this into perspec­ tive, let us consider treatment costs for organ transplants, bone grafts, and osteoarthritis. Approximately, one in five people in the United States requires organ replacement before age 65. Likewise, approximately one million bone grafts are performed each year in the United States, at an estimated annual cost of exceeding $3 billion [1]. Osteoarthritis, a leading chronic disability in the middle‐aged population, affects approximately 70 million people [2], at an esti­ mated annual treatment cost exceeding $65 billion [3]. Further, in addition to costs, soft tissue defects present traumatic challenges. For example, resection to treat breast cancer leaves patients disfigured. As such, there is a desperate clinical need to improve treatment strategies through more effective, special­ ized, and less invasive techniques. As it stands, TE involves lengthy sequential procedures and requires sequen­ tial assessment of the engineering outcome and patient response. Tissue engi­ neers commonly evaluate cell morphology and activity through the use of microscopy and histology. While these histological tools provide a representa­ tion of molecular content and signaling pathways—for example, staining for alkaline phosphatase (ALP) serves as an early osteogenic marker—they are Magnetic Resonance Imaging in Tissue Engineering, First Edition. Edited by Mrignayani Kotecha, Richard L. Magin, and Jeremy J. Mao. © 2017 John Wiley & Sons, Inc. Published 2017 by John Wiley & Sons, Inc.

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destructive to the tissue [4]. Therefore, dynamic monitoring of developing ­tissue properties through noninvasive assessment tools can provide additional means to analyze the patient’s complex progression through treatment and increase the quality of engineered tissues. Different imaging modalities have been investigated for use in TE, but each has limitations. For example, optical and fluorescent microscopies have low‐ penetration depth. Ultrasound incurs low spatial resolution. Micro‐computed tomography (CT) introduces harmful ionizing radiation [5]. On the other hand, magnetic resonance imaging (MRI) has been shown to have the potential to quantify the properties of the engineered tissue, such as calcium deposition in TE bone. MRI does this through mechanisms that include T1, T2, diffusion, and magnetization transfer (MT). Likewise, a comprehensive review of MRI contrast mechanisms in TE has been presented in multiple articles [6]. Overall, MRI applications have the potential to improve TE practices and reduce harm to the patient. Although conventional MRI techniques have made significant contribution to the field of TE, one of the key factors for successful TE practices is to ensure that the engineered substitute exhibits the same strength and structure as the original. For example, tissue engineers have identified that in order for any material or TE construct designed for potential human implantation to achieve and maintain proper functionality, they must retain adequate mechanical properties (i.e., hardness, elasticity, and yield strength) [7]. Understanding of material performance is critical since during TE construct growth, mechanical properties change due to cellular responses and material remodeling, includ­ ing stem cell differentiation, cellular contraction, scaffold remodeling, and biodegradation of the scaffold. In other words, it is necessary for imaging methods to adapt to the needs of tissue engineers, such as gauging tissue mechanics during TE continuously and noninvasively. Fortunately, elastography imaging techniques, such as magnetic resonance elastography (MRE), have the potential to address the understudied area of noninvasive mechanical assessment of developing TE constructs. MRE measures shear wave motions in biological tissues, providing unique, spatially localized, and quantitative characterization of material properties of tissue [8, 9]. MRE has been implemented clinically both to detect breast cancer [10] and to visualize the elastic properties of the brain [11]. MRE was extended to the microscopic scale (μMRE) that provides high resolution for small‐sized tis­ sue samples [12]. This chapter discusses the current and future role of MRI and MRE in moni­ toring tissue‐engineered constructs. First, a brief introduction of microscopic MRE (μMRE) is presented. Second, current applications of μMRE in vitro and in vivo TE of mesenchymally derived constructs are presented, where Figure 5.1 shows potential applications of MRE in TE.

5.2 ­Introduction to MR Microscopic monitoring

MRE in vitro

Elastogram

Histology

MRE in vivo

TE Construct

Cellular expansion

Differentiation and development

Implantation

In vivo regeneration

Figure 5.1  Flow diagram for the differentiation and proposed MRE/MRI monitoring of engineered constructs. First, the cells are expanded in vitro and monitored using traditional microscopy. Next, the cells are seeded to a scaffold and the appropriate differentiation reagents are applied. Cells grow and differentiate inside the construct over a specified period and tissue development is observed using in vitro MRE/MRI. Then the constructs are implanted in animal models and monitored via in vivo MRE/MRI combined with histologic analyses.

5.2 ­Introduction to MRE Palpation is a well‐recognized diagnostic tool used by physicians to determine the difference in the mechanical properties of tissues and to differentiate between abnormal and normal tissues. Quantitatively, the mechanical proper­ ties of any material can be determined from its response to applied forces. To measure the mechanical properties of a soft tissue, several tools for uniaxial, biphasic, and three‐dimensional assessment have been applied to tissues ex vivo [13]. One major disadvantage of these methods is that they are invasive. The mechanical properties of tissues vary widely in different physiological and pathological states. With this in mind, palpitation has significant diagnos­ tic potential. For example, the elastic modulus of a given tissue can vary by over five orders of magnitude, while the tissue properties assessed by other modali­ ties, such as CT, MRI, and ultrasonography (US) vary over a much smaller scale. Further, the relative hardness, and not the mechanical properties, of malignant tumors is the basis for the use of palpation to detect breast cancer [14]. Likewise, surgeons often use simple touch at laparotomy to detect liver tumors, which may not have been detected by preoperative imaging. However,

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other than during surgery, palpation is only applicable to superficial organs and pathologies. Further, palpation is qualitative, subjective, and limited to the touch sensitivity of the practitioner. However, none of the conventional medical imaging techniques (i.e., CT, MRI, or US) are capable of depicting the proper­ ties that are assessed by palpation. These considerations have motivated engineers to develop special imaging technologies that quantitatively and noninvasively assess the mechanical properties of tissue. MRE was first introduced by Muthupillai [8, 9]. This technique typically uses vibrations of a single frequency generated by external driver devices. The elec­ trical signal for such device is created by a signal generator triggered and syn­ chronized to the MRI pulse sequence. The electrical signal is then amplified before being fed into the mechanical driver. Presently, MRE applications are under investigation for a multitude of diseases that affect the tissue stiffness. One of the main applications of MRE is the investigation of hepatic disease diagnosis [15]. The technique is currently used in clinical practice for fibrosis and cirrhosis assessment as it was found that the stiffness of a diseased liver is significantly higher than normal liver tissue stiffness. It was documented that the stiffness of the liver is directly related to the fibrosis stage and it increases with the progression of the disease; a cutoff of 2.93 kPa was found to be an optimal threshold for distinguishing healthy livers from fibrotic ones [15]. The current MRE technique essentially involves the following three steps. First, an acoustic or mechanical actuator is coupled to the tissue of interest to induce cyclic shear motion with low amplitude (typically up to 100 µm). Second, an MRI pulse sequence, using motion‐encoding gradients (MEG) synchronized with the harmonic excitations, is used to encode the motion. This provides images whose intensity at every voxel represents the local displacement or strain undergone by the tissue due to the propagating shear waves. Third and finally, an inversion algorithm is applied to evaluate local values of mechanical properties based on these displacement maps and applied mechanical model. 5.2.1  Theoretical Basis of MRE

MRE is a phase‐contrast MRI technique that measures the displacement of soft tissue spins subjected to cyclic mechanical excitations [8]. However, in a gen­ eral MRI acquisition, the phase image is contaminated by system imperfec­ tions and applied gradients, such as eddy current or physical effects such as magnetic susceptibility. For this reason, phase images are generally represented by graphical plots of the differences between the phases of two sets of data. For given images A and B, this could be the following equation: x ,y

A

x ,y

B

x ,y . (5.1)

The phase information can be utilized to generate a contrast related to tissue’s spin motion. This can be completed by intentionally introducing controlled

5.2 ­Introduction to MR

motion and using gradient filtering to study this motion. Because the spins in a magnetic field possess a phase that depends on the strength of the magnetic field and the applied magnetic field along the direction of the spin motion [16], the phase equation can be correlated to the spin motion by the following equation:



  B0 r t dt

  GE r t dt

0

0

  Gr t r t dt , (5.2) 0

 In Equation 5.2, γ is the gyromagnetic ratio characteristic of the nuclei, B0 is  the static magnetic field strength, GE (t ) is one of the encoding gradient vectors,   r (t ) is the spin motion vector, and Gr (t ) is the bipolar gradient vector which is collinear to the encoding gradient and used for spin motion filtering. This gradient is also called the motion‐sensitizing gradient. The phase due to the motion is as follows:



  Gr t r t dt . (5.3) 0

A cyclic‐induced displacement will cause a temporal spin motion vector ­governed by the following:     r t r0 r ,t , (5.4)  this case, r0 represents the initial location of the spin at time t = 0, and  In  (r ,t ) is the cyclic displacement of the spin about its mean position caused by the mechanical excitation. If the motion‐sensitizing gradient, with multiple bipolar pairs N and dura­  tion τ, is synchronized at the same frequency  withthe induced spin motion, (r ,t ), and turned on for a duration τ so that Gr (t ) r0 dt 0, then the dynamic phase 0 shift of the moving magnetization, Equation 5.3, can be rewritten as follows:



 Gr t

  r ,t dt . (5.5)

0

  Further, the cyclic displacement vector (r ,t ) can be written as follows:   r ,t

 

0e

 j kr

t

, (5.6)

  In Equation 5.6, o is the peak displacement of the spin from the mean posi­  tion, k is the wave vector, ω is the angular frequency of the mechanical wave excitation, and ψ is the introduced phase offset between the bipolar gradient pulses and the wave.

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As an  example, the bipolar gradient can be set to be sinusoidal, Gr (t ) G0 cos( t ), reducing the equation to a phase constant that is a function of position and phase shift, as follows:  G0 cos

 r, 0

t

   NT G0 0



2

 

0e

 j kr

  cos k r

t

dt

(5.7)

,

In Equation 5.7, the motion‐sensitizing gradient duration was written as NT , with N being the number of bipolar pairs, and T 2 / . By examining Equation 5.7, the measured phase magnitude depends on the number of bipo­ lar pairs N, which is the dot product of the amplitude of the bipolar gradient and the peak displacement spin amplitude motion (G0 0 ) modulated by the cosine function. The dot product relationship indicates that the bipolar gradi­ ent acts as a filter to extract its collinear displacement. Moreover, the measured phase shift is related to the initial phase offset ψ between the gradient wave­ form and the mechanical excitation. Therefore, by varying ψ over one period, it is possible to study the temporal evolution of spin displacements. MRE pulse sequence can be integrated in conventional MRI pulse sequences such as gradient‐echo or spin‐echo pulse sequences by synchronizing the MEG with the mechanical actuator. A typical gradient‐echo‐based MRE pulse sequence is shown in Figure 5.2, with the conventional radiofrequency pulse waveform, slice‐selection gradient, phase‐encoding gradient, and frequency‐ encoding gradient. The MEGs are shown in the frequency‐encoding direction only, but motion that occurs in any direction can be encoded into the phase of the MR image by manipulating the axes on which the MEGs are placed. Two wave images are typically collected with a positive and a negative MEG, and a phase‐difference image is calculated, either by subtraction or complex division, to remove all phase information not related to the imparted motion. In order to acquire time‐dependent data and show the propagation of the wave through the tissue of interest, multiple phase offsets ψ, evenly distributed over a period of the mechanical excitations, are selected. The motion‐encoding capability of this technique is highly sensitive and can detect motion on an order of 100 nm [9]. Various groups have developed different pulse sequences based on spin‐ echo, gradient‐recalled echo, balanced steady‐state free precession, and echo‐ planar imaging [17]. 5.2.2  The Inverse Problem and Direct Algebraic Inversion

The inversion method is based on a previously developed MRE technique [18, 19]. The essence of the technique is that mechanical properties can be obtained by direct inversion of a differential equation of motion. Conservation of momentum

5.2 ­Introduction to MR Data collection Magnitude image

Masking and phase unwrapping

Noise reduction 1% gel Spatiotemporal filtering

40 mm

Estimation of material properties

0.5% gei

Low-pass filter

25 mm

Selection of ROI

Figure 5.2  Magnitude image of the gel phantom used to provide the example on describing the direct inversion algorithm.

provides the dynamic equation of motion that relates material properties to displacement. Ignoring body forces, momentum conservation written in a Lagrangian coordinate system is as follows: Div

a ,t

a

2

u a ,t t2

, (5.8)

In this case, σ is the Cauchy stress tensor describing internal forces that arise due to the strain of the material, ρ is the density, a is the location, and u is the displacement. The given displacement on the boundary u* is time dependent, so that we have the following equation: u* t u* t d , (5.9) In this case, d is the unit displacement direction vector. Given the harmonic nature of the imparted motion, this displacement boundary condition can be represented by Fourier series, such that

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u* t

a0

an2 bn2 cos n 0t

n 1

n

, (5.10)

u * t cos

2n t dt , n 1, 2, , (5.11) T

with a0

1 T

T /2

2 T

T /2

2 T

u * t dt , an

T /2

T /2

T /2

and bn

u * t sin

T /2

2n t dt , n 1, 2, , (5.12) T

and where 2 , cos T

0



an

n

an2

bn2

, sin

n

bn an2

bn2

(5.13)

The boundary conditions must be satisfied for each frequency nω0 in the frequency package (n = 1, 2, …). Thus, from here, only monochromatic dis­ placements and strains will be considered. For simplicity, the tissue of interest is modeled as a linear, Hookean, and viscoelastic solid. In a linear viscoelastic solid, stress σ and strain ε are related by the fourth‐order tensor E, so that we have the following:

t

t 0

E t s :d

s

t 0

E t s :  s ds E t :

0 . (5.14)

Assuming monochromatic displacements, this relation can simply be writ­ ten as follows:

t

E* :

t , (5.15)

where * Eabkl

Eabkl 0

sin

d

i

Eabkl

cos

d . (5.16)

0

For a linear viscoelastic and isotropic material model, the complex‐valued fourth‐order tensor E* contains 81 components. Because of the symmetries of the stress and strain tensors, as well as the major symmetry of the stiffness tensor, the components of E* have the following properties: * Eijkl

E *jikl

* Eijlk

* Eklij , (5.17)

Hence, reducing the number of independent components to 21, in the most general case. For an isotropic material, the number of independent constants

5.2 ­Introduction to MR

in tensor E* further reduces to 2, which can be chosen to be the complex‐ valued Lamé constants λ and μ. In this case, we have the following: * Eijkl a, f

a, f

ij

kl

a, f

ik

jl

il

jk

,

(5.18)

Here, δij is the Kronecker delta. The strain is related to the displacement by the following:

ij

1 ui , j u j ,i . (5.19) 2

In the case of small harmonic oscillations of frequency f, the temporal deriva­ tive is simply a multiplication by i2πf, such that 2



u a ,t t

4

2

2

f 2 u a, t (5.20)

Thus, conservation of momentum can be rewritten in the frequency domain in the case of a small‐amplitude, broadband, and harmonic motion in isotropic materials in the following form: 2 f

2

0

a ui a, f a, f u j , j a, f

i

a, f

ui , j a, f

u j ,i a, f

j

.

(5.21) This is the partial differential equation assumed for broadband, source‐free, and small‐amplitude motion in an isotropic, viscoelastic material. The method used for estimating Lamé constants consists of inverting the given equation by assuming local homogeneity to transform it into an algebraic equation for each spatial position a and temporal frequency f. This yields the following: ui ,i1 a, f

u1,ii a, f

ui ,i 2 a, f

u2 ,ii a, f

ui ,i 3 a, f

u3,ii a, f

a, f

a, f a, f

u1 a, f 4

2

f

2

a u2 a, f

.

u3 a, f

(5.22) If we further assume planar shear wave propagation, the terms containing derivatives with respect to the third dimension will be zeroed out. Under this assumption, the equations decouple into two independent two‐dimensional modes. Each mode can be used to find μ(a, f ) independently. The simplest inversion scheme occurs with measurements of the out‐of‐plane shear mode for a purely two‐dimensional problem. In this case, local inversion of this expression can be accomplished with a single component of motion by

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computing a ratio between the out‐of‐plane displacement and its Laplacian (from the third equation) given by the following equation: 2

f2

a u3 a, f

u3,11 a, f

u3,22 a, f

4

a, f

.

(5.23)

Storage and loss moduli can be derived from this complex‐valued parameter, which are its real and imaginary parts written as follows:

a, f

a, f

i

a , f .

(5.24)

It should be noted that estimation of these quantities does not require the use of any rheological model (e.g., Voigt, Kelvin, and standard linear solid). For improved understanding of the estimates, it can be useful to report two other equivalent parameters: the shear wave speed cs and attenuation α of a hypothetical plane‐wave propagating through an infinite medium made up of the same material. For a single‐frequency plane‐wave propagating in an infi­ nite medium, the wavenumber k0 is given by the following equation: 2 k0

2 f cs

i . (5.25)

In the case studied here, the wavenumber can be expressed as

2 k0

2

u3 . (5.26) u3

2

Thus, the Equations 5.25 and 5.26 can be combined to give a, f

2 cs a , f

a, f

2 f

2

a

2

a 2

a, f

a, f

2

a, f

a

2

a, f

2

a

a 2

a, f



a, f

a a, f

,

(5.27)

a 2

a, f

a a, f

2

2

a .

a

(5.28)

5.2 ­Introduction to MR

The shear stiffness can also be calculated which combines the storage and loss moduli and it is the equivalent of the shear modulus for an elastic material. It is defined as follows: S a, f

a cs a , f

2

.

(5.29)

The MRE inverse problem can be solved using a direct algebraic inversion algorithm or a finite‐element approach. The first method is discussed with an example in the following section. 5.2.3  Direct Algebraic Inversion Algorithm

The direct algebraic inversion will be described using the example of an experi­ ment performed on a gel phantom composed of two layers of 1 and 0.5% in agarose gel, as shown in Figure  5.2. The algorithm is based on a previously developed MRE spatiotemporal filtering approach [19]. The first step of the algorithm is to apply eventual masking and phase unwrapping to the raw data. Masking can either be completed by manually drawing the contours of the mask or by adjusting the threshold based on the magnitude image. Several options are also available to unwrap the phase images. The first one can be obtained by taking the two‐dimensional Fourier transform of the image. The unwrapped phase  at any pixel (i, j) is given by the following equation:  i, j



i, j , (5.30) i j 2 cos 2 cos 4 M N In this case,  is the wrapped phase, and N and M are the dimensions of the

image. A two‐dimensional inverse Fourier transform allows for recovery of the unwrapped phase image. The other available method aims at creating a quality map from the phase derivative variance. The pixel with the highest quality is then utilized as a starting point, and the image is unwrapped pixel by pixel, following a path determined by the quality map. The masked and unwrapped phase images (in this case eight images evenly distributed over one cycle of the mechanical excitations) are shown in Figure 5.3. A low‐pass filter is applied to remove the noise present in the raw data which is composed of high spatial frequencies. For this purpose, a Gauss blur is used, substituting the value of each pixel by the weighted average of its 25 neighbors, weights being calculated from a Gauss distribution law. The standard deviation of the Gauss distribu­ tion can be modified to adjust the cutoff frequency, as shown in Figure  5.4. Additionally, a low‐pass filter is applied to remove the noise present in the raw data. A Gauss blur is used, substituting the value at each pixel by the weighted average of its 25 neighbors, with weights calculated using a Gauss distribution law. The standard deviation of the Gauss distribution can be modified to adjust the cutoff frequency.

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Figure 5.3  Shear wave images eight different phase offsets, after masking and phase unwrapping.

20

40

60

Gauss blur on displacement maps (µm) 50

Data collection 0 Masking and phase unwrapping

Noise reduction

Gauss blur

–50 Standard deviation: 0.8

Spatiotemporal filtering

Raw data Blured data

Estimation of material properties

Low-pass filter

Selection of ROI

Standard deviation: 1.5

Figure 5.4  Reduction of the electronic noise present in the raw data by Gauss blur.

Line profile

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5  Magnetic Resonance Elastography Applications in Tissue Engineering

Next, a spatiotemporal filter is applied to select one temporal frequency and cancel reflected waves. Then, a three‐dimensional Fourier transform is computed, and the temporal plane that corresponds to the frequency of the actuator is selected based on the number of points of the Fourier transform and the sampling frequency. The other planes are zeroed out because they carry information about waves propagating at other temporal frequencies. In this plane, a wave propagating in a specific direction is represented by a peak at the location (kx, ky) corresponding to its two spatial frequencies. The same wave propagating in the opposite direction can be represented by a peak at the symmetric location (−kx, −ky). For instance, a wave propagating from top to bottom will be such that kx = 0 and ky > 0, whereas the exact same wave propagating from bottom to top will be such that k x  = 0 and k y  = −ky. Thus, to determine the top‐down direction, a map of coefficients can be created to emphasize particular propagation pathways, as shown in Figure  5.5. The coefficients have a cosine square (cos2) dependence about the selected direction in one half‐plane and are zeros in the other half‐plane. This filter is applied to the selected temporal plane, and the inverse three‐ dimensional Fourier transform is computed. Imaginary parts are discarded as they are caused by dissymmetry of the spectrum. The real parts of the first eight images represent the filtered wave, which propagates at the fre­ quency of the actuator in the selected direction. Third‐order Savitzky–Golay polynomials are applied to both the vertical and horizontal directions to further improve the smoothness of the displacement field. Next, the spatial second derivatives, which are approximated with the central difference method of second order, are estimated with the following equations: 2

x2

2



u n, m u n, m y2

u n 1, m

2u n, m

u n 1, m , (5.31)

u n, m 1

2u n, m

u n, m 1 . (5.32)

The complex‐valued shear modulus is then computed on a pixel‐by‐pixel basis, and eventual negative real parts are zeroed out. This algorithm provides accurate results when waves mainly propagate in a particular direction, which can be the case for experiments on gel phantoms. However, biological tissues are highly anisotropic and heterogeneous, providing complex pathways for mechanical waves. To account for such materials and improve the faithfulness of the recovery when studying complex wave patterns, eight different direc­ tions of propagation (every 45°) are filtered out using the previously described spatiotemporal filter (Figure 5.6). The complex‐valued shear modulus is then

Filter coefficients 1 y

Data collection

Masking and phase unwrapping

t

0.5

3D FFT Noise reduction Plane selection Spatiotemporal filtering

Estimation of material properties

3D FFT

Directional filtering iFFT

0

x Filtered wave (µm)

40

ky 0

Low-pass filter f Selection of ROI

–40 kx

Figure 5.5  Spatiotemporal filtering. In the example shown, the extracted direction is from top to bottom.

Filtered wave (µm) 60

0

–60

Data collection

Masking and Phase unwrapping

0°

Noise reduction 315° Spatiotemporal filtering

Estimation of material properties

Low-pass filter

1% gel

Savitzky–Golay polynomials Second spatial derivatives Material parameters

45°

270°

90° 0.5% gel

8× 225°

135° 180°

Selection of ROI

Figure 5.6  Material properties are calculated from each filtered dataset and averaged with a weight corresponding to the amplitude of the motion at each pixel. (See insert for color representation of this figure.)

5.2 ­Introduction to MR Data collection Masking and phase unwrapping

Stiffness (kPa)

Smoothed stiffness (kPa)

30

30

15

15

0

0

Noise reduction Spatiotemporal filtering Estimation of material properties Low-pass filter

Circular Butterworth

Selection of ROI

Figure 5.7  A circular low‐pass Butterworth filter is applied on every map of material properties so that they appear smoother. (See insert for color representation of this figure.)

estimated from these different datasets and averaged on a pixel‐by‐pixel basis, with a weight depending on the relative intensity of each filtered dataset at this location. Other mechanical parameters can be deduced from this quantity. These mechanical parameters include the storage and loss moduli, the shear wave speed and attenuation, as well as the shear stiffness. Next, every map of mechanical parameters can be smoothened by applying additional low‐pass filters (Figure 5.7). Circular Butterworth filters are used, and their diameter (or cut‐off frequency) is adjusted to specify the desired level of details conserved in the final images. Algorithms performing an algebraic inversion of the differential equation of motion, such as the one described before, have been developed based on a planar shear wave assumption. This assumption will be satisfied with a point vibration source in an infinite and homogenous solid material, where the observation is made far away from the source. In a viscous finite solid where the boundary conditions can interfere with the shear wave propaga­ tion, the plane wave assumption begins to break down. A finite‐element‐ based approach discussed in Chapter  6 would have the advantage of providing a volumetric model where this assumption is not required, and might allow the use of different material models for relating stress to strain [20] (see Chapter 6).

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5.3 ­Current Applications of MRE in Tissue Engineering and Regenerative Medicine 5.3.1  In Vitro TE μMRE

For monitoring in vitro TE constructs, microscopic MRE is typically applied [21, 22]. With tissue development, the structural and mechanical characteris­ tics of engineered constructs are expected to change and these transforma­ tions are the basis for using MRE in TE. For example, these transformations are apparent in the wide dynamic range in their mechanical properties (e.g., Young’s modulus), which span several kPa for adipogenic tissues to hundreds of kPa for chondrogenic tissues and tens of MPa for fully developed osteogenic tissues. Recent studies were applied to monitor mesenchymally derived constructs including bone and fat. MRE systems for monitoring TE constructs were developed at 11.7 T [12, 21] and 9.4 T [22]. Both MRE systems utilized a modi­ fied gradient or spin‐echo sequences to acquire the spin motion by synchro­ nizing a mechanical actuator with the motion‐sensitizing gradients. A complete description of the design and implementation of the MRE system can be found in multiple previous publications [23]. Both systems described before were applied for assessing engineered con­ structs fabricated by seeding human mesenchymal stem cells (MSCs) onto gelatin sponges with appropriate osteogenic and adipogenic differentiation factors. Briefly, mesenchymally derived constructs are prepared by seeding MSCs isolated from fresh commercial adult bone marrow. Nucleated cells are incubated in a basic culture medium, commonly supplemented with fetal bovine serum (FBS), antibiotics, penicillin, streptomycin at 37°C, and 5% CO2. A biological scaffold such as biodegradable sterile gelatin sponge is trimmed into desirable physical size of tissue constructs. Constructs are generated by seeding the scaffold with hMSCs. The experimental group is cultured in osteogenic or adipogenic induction media to stimulate specific differentiation, as opposed to the control cultured in the basic culture medium only. In monitoring the development of adipose TE constructs in gelatin‐based scaffolds, a factor of four stiffness reductions was measured at week 3 com­ pared to week 1 as shown in Figure 5.8 [23]. In comparison, conventional MR techniques were not effective in differentiating engineered adipogenic tis­ sues due to the limited structural change in the extracellular matrix (ECM) of the tissue. This demonstrated the high sensitivity of μMRE for evaluating developing tissues. In comparison, osteogenic‐derived tissues hardened with tissue culture from 4 to 18 kPa [20]. Increasing stiffness of osteogenic con­ struct was attributed to mineral deposition [21], while softening of the

5.3 ­Current Applications of MRE in Tissue Engineering and Regenerative Medicin (b)

(c)

(b)

(c)

10 mm

(a)

8 mm

Figure 5.8  Shear wave images and their corresponding Oil Red‐O staining for stem cell– agarose constructs. (a) MRI magnitude image at two growth stages representing significant changes in mechanical properties at (b) 1 and (c) 3 weeks with their corresponding staining where cells were stained for Oil Red‐O to indicate lipid synthesis as red (dark color) and counterstained with Hematoxylin to mark nuclei blue (lighter color). The shear wavelength reduced significantly with differentiation where the location of the fat specimen was identified based on the MR magnitude image. In‐plane resolution = 109 mm × 109 mm, slice thickness = 0.5 mm, and NEX = 1.

adipogenic construct was attributed to increasing glycerol content where the overall stiffness decreased as lipids are accumulated within the scaffold. Another study conducted at 9.4 T generated time‐course elastograms of osteogenic and adipogenic tissue constructs as shown in Figure  5.9 [24]. Results are obtained that show the change in shear stiffness for both osteo­ genic and adipogenic constructs based on the application of MRE analysis. The main advantage of this technique over existing methods, like mechanical testing, is that it applies the noninvasive technology of MRI to assess mechan­ ical properties that are normally tested invasively. This method can help answer key questions in the TE, such as what factors most significantly affect tissue stiffness. μMRE‐generated high‐resolution spatial maps of the shear stiffness for the assessment of the mechanical properties noninvasively in  vitro will allow us to generate TE constructs with adequate strength to withstand in vivo regeneration.

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Figure 5.9  Construct development map over 4‐week period. Adipogenic (A) and osteogenic (O) constructs are shown from left to right with corresponding shear wave image, elastogram, and average shear stiffness. The colormap for the elastogram corresponds with the color scheme of the bar chart. (See insert for color representation of this figure.)

5.3.2  In Vivo TE μMRE

In preliminary studies, μMRE has proven to be an effective method for repeat­ edly gauging the mechanical properties of regenerating mesenchymally derived TE constructs in vivo. In one example, the MSC gelatin‐based scaffolds were implanted subcutaneously in 8‐week‐old male immunodeficient mice following in vitro development. A piezoceramic actuator was coupled to the skin of the mouse adjacent to the implanted construct inside the radiofrequency imaging coil, enabling direct stimulation of shear wave motion in live animals as shown in Figure 5.10 [24]. During the MR experiment, the mice were anesthetized with 1–2% isoflurane with vital signs monitoring. Monitoring and gating included electrocardiogram (ECG), respiration, and pulse oximetry. The body tempe­ rature was traced using a temperature probe and adjusted via an air heater. The MRE experiments were conducted at 9.4 T. An actuator was designed to ­provide sufficient displacement into the construct. The design was achieved through the use of a piezoelectric bending motor secured to a curvature around the mouse. A plastic cap was attached to the other side of the piezoelectric motor and coupled to the mouse’s body adjacent to the tissue construct.

5.3 ­Current Applications of MRE in Tissue Engineering and Regenerative Medicin Trigger pulse Function generator

Amplifier

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Figure 5.10  The MRE in vivo experimental setup with preliminary results for in vivo osteogenic construct. (a) A spin‐echo‐based phase‐contrast imaging sequence was modified to incorporate the sinusoidal bipolar gradient pulses (MSG) that synchronously trigger a piezoelectric actuator, attached to the construct, and extract the strain generated by spin motion. The actuator was designed to be placed adjacent to the construct for maximum wave propagation. The arched suspension design permitted the actuator to be rotated over the curvature of the mouse’s body to optimize positioning. Experimental sagittal, coronal, and axial views are shown. (b) Osteogenic construct imaged 4‐weeks post implantation with shear wave images acquired with delays of π/2 and 3π/2 with the corresponding MR coronal image.

Before  placement in the scanner, the actuator was characterized while the  mouse is placed on the holder using a laser Doppler vibrometer and its ­resonance frequency providing the largest dynamic amplitude is identified. MRE acquisitions were carried out with the addition of external respiration gating to reduce motion artifacts. In summary, performing in vivo MRE requires overcoming many hurdles including the characterization of the mechanical actuator while it is attached to the animal and gating the MRI sig­ nal to negate the effect of respiratory and cardiac motion as needed. Another example is shown in Figure 5.11 shows shear wave images (top) and elastograms (bottom) of mesenchymally derived adipogenic, chondrogenic,

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5  Magnetic Resonance Elastography Applications in Tissue Engineering

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Chondrogenic

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Figure 5.11  Shear wave images (top) and corresponding stiffness maps (bottom) in engineered constructs after 4 weeks of implantation. The displacement map shows the propagation of shear waves through constructs. Notice that, multiple waves are visible in adipose construct, indicating a lower stiffness and softer tissue structure, while for stiffer tissues—both osteogenic and chondrogenic—a full shear wave is not attained. Reconstructed elastogram on the bottom shows estimated stiffness of 2, 9, 15 kPa for adipogenic, chondrogenic, and osteogenic constructs, respectively. (See insert for color representation of this figure.)

and osteogenic constructs 2‐weeks post implantation using a mechanical actuator with 915 Hz excitation frequency [25]. This figure demonstrates the superior contrast provided by μMRE where the estimated shear stiffness is 2, 9, 15 kPa for adipogenic, chondrogenic, and osteogenic constructs, respectively [25]. Multiple waves are apparent in the adipogenic shear wave image indica­ tive of a soft tissue represented by the blue color in the elastogram compared to the stiffer osteogenic and chondrogenic tissues represented with the red/ yellow elastograms. The inferior mechanical properties compared to normal tissues for both osteogenic and chondrogenic constructs in Figure 5.11 might be due to the short implantation duration and suggest utilizing different TE strategies including the use of different scaffolds. The engineering outcome will depend on the scaffold material and physical properties, including the pore size and the scaffold composition. To confirm the sensitivity of μMRE in assessing the difference in mechanical properties of different implanted constructs, three different scaffolds were selected for car­ tilage TE: gelatin sponges with a pore size of 250 µm (Pharmacia & Upjohn,

5.3 ­Current Applications of MRE in Tissue Engineering and Regenerative Medicin T2 relaxation map

20 mm 4.9 mm

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Figure 5.12  Silk construct development map over 8‐week study. Shown from left to right are the magnitude image, T2 relaxation map, shear wave image, and stiffness map of the constructs. Average T2 relaxation times decreased from 91.2 67.6 ± 3.1 at week 8. Average stiffness values increased from 7.6 ± 2.0 kPa 17.2 ± 3.1 at week 8. (See insert for color representation of this figure.)

Kalamazoo, MI), fabricated protein silk with a pore size of 500 µm (Department of Biomedical Engineering, Tufts University, Medford, MA), and collagen con­ structs with a pore size of 350 µm (Kensey Nash, Exton, PA). The constructs were cultured for 3 weeks before implantation. Tissue implantation was per­ formed as described before. The in vivo results indicate variations of imaging parameters during regeneration and differentiation of the chondrogenic con­ structs toward cartilage. Average stiffness values increased by a factor of 2.5 for the collagen scaffold, while it increased by a factor of 4 for the gelatin scaffold and a factor of 2 for the silk scaffold as shown in Figures 5.12 and 5.13 [26]. However, it should be mentioned that the silk construct is the only one that maintained its original anatomical shape during the entire study. In vivo MRE provides a noninvasive rapid‐feedback method for improving the tissue‐engineered outcome. The developed method allows sacrificing smaller number of animals per experiment. In a longitudinal experiment, each animal is studied as its own control, thereby enabling repeated evaluation of regenerating features and more measureable statistics. Additionally, it is pos­ sible to correlate MR parameters, stiffness measurement, with different geno­ typic markers. For example, in that study, the expression of collagen II, Agg, and COMP were significantly upregulated within silk, collagen, and gelatin scaffold after 8‐weeks in vivo culture, compared with constructs cultured in vitro for 3 weeks without differentiation [26]. In vivo MRE has the potential to contribute to the field of TE after validating mechanical assessment by signifi­ cantly reducing the number of studied animals to increase the effectiveness of the statistical analysis. This will significantly reduce the cost specially when moving to large animal models necessary for clinical translation.

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5  Magnetic Resonance Elastography Applications in Tissue Engineering T2 relaxation map

20 mm

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Figure 5.13  Collagen construct development map over 8‐week study. Shown from left to right are the magnitude image, T2 relaxation map, and stiffness map of the constructs. Average T2 relaxation times decreased from 75.2 ± 18.4 ms at week 2 to 58.4 ± 4.2 at week 8. Average stiffness values increased from 4.6 ± 1.7 kPa at week 2 to 14.7 ± 3.8 kPa at week 8. (See insert for color representation of this figure.)

5.4 ­Conclusion The studies presented in this chapter illustrate the potential of μMRE as a new technique for assessing the mechanical properties of engineered tissues. The application of μMRE provides new contrast mechanisms that minimize the need to replicate experiments to obtain statistically valid research from in vitro to animal models. Thus, experiments can be conducted using the same sample repeatedly, reducing inconsistency, and increasing the statistical power during hypothesis testing. While standard histological methods are still important for validating the outcome of tissue implants, we believe that with fine‐tuning, μMRE has the potential in many situations to replace histology. Finally, the implications of this technique extend toward therapy (or diagnosis) of tissue regeneration because assessment of a material’s mechanical properties will ensure that the material is appropriate for its intended use, and that, when implanted is successful in handling the applied loads, ensuring the successful restoration and replacement of cartilage.

­References 1 Laurencin CT, Khan Y, Kofron M, El‐Amin S, Botchwey E, Yu X, et al. The

ABJS Nicolas Andry Award: Tissue engineering of bone and ligament: a 15‐year perspective. Clinical Orthopaedics and Related Research. 2006;447:221–36.

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3 Mao JJ. Stem‐cell‐driven regeneration of synovial joints. Biology of the Cell.

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4 Sabokbar A, Millett PJ, Myer B, Rushton N. A rapid, quantitative assay for

measuring alkaline phosphatase activity in osteoblastic cells in vitro. Bone and Mineral. 1994;27(1):57–67. 5 Wolbarst AB, Hendee WR. Evolving and experimental technologies in medical imaging. Radiology. 2006;238(1):16–39. 6 Xu, H, Othman SF, Magin RL. Monitoring tissue engineering using magnetic resonance imaging. Journal of Bioscience and Bioengineering. 2008;106(6):515–27. 7 Butler DL, Hunter SA, Chokalingam K, Cordray MJ, Shearn J, Juncosa‐Melvin N, et al. Using functional tissue engineering and bioreactors to mechanically stimulate tissue‐engineered constructs. Tissue Engineering Part A. 2009;15(4):741–9. 8 Muthupillai R, Ehman RL. Magnetic resonance elastography. Nature Medicine. 1996;2(5):601–3. 9 Muthupillai R, Lomas DJ, Rossman PJ, Greenleaf JF, Manduca A, Ehman RL. Magnetic resonance elastography by direct visualization of propagating acoustic strain waves. Science. 1995;269(5232):1854–7. 10 McKnight AL, Kugel JL, Rossman PJ, Manduca A, Hartmann LC, Ehman RL. MR elastography of breast cancer: preliminary results. American Journal of Roentgenology. 2002;178(6):1411–7. 11 Braun J, Buntkowsky G, Bernarding J, Tolxdorff T, Sack I. Simulation and analysis of magnetic resonance elastography wave images using coupled harmonic oscillators and Gaussian local frequency estimation. Magnetic Resonance Imaging. 2001;19(5):703–13. 12 Othman SF, Xu H, Royston TJ, Magin RL. Microscopic magnetic resonance elastography (microMRE). Magnetic Resonance in Medicine. 2005;54(3):605–15. 13 Mow VC, Kuei SC, Lai WM, Armstrong CG. Biphasic creep and stress relaxation of articular cartilage in compression? Theory and experiments. Journal of Biomechanical Engineering. 1980;102(1):73–84. 14 Barton MB, Harris R, Fletcher SW. The rational clinical examination. Does this patient have breast cancer? The screening clinical breast examination: should it be done? How? JAMA: The Journal of the American Medical Association. 1999;282(13):1270–80. 15 Singh S, Venkatesh SK, Loomba R, Wang Z, Sirlin C, Chen J, et al. Magnetic resonance elastography for staging liver fibrosis in non‐alcoholic fatty liver disease: a diagnostic accuracy systematic review and individual participant data pooled analysis. European Radiology. 2015;26(5):1431–40. 16 Haacke E, Brown R, Thompspn M, Venkateson R. Magnetic Resonance Imaging: Physical Principals and Sequence Design. New York: John Wiley and Sons; 1999.

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elastography: a multiecho phase‐contrast gradient‐echo sequence. Journal of Magnetic Resonance Imaging. 2006;23(5):774–80. Oliphant TE, Manduca A, Ehman RL, Greenleaf JF. Complex‐valued stiffness reconstruction for magnetic resonance elastography by algebraic inversion of the differential equation. Magnetic Resonance in Medicine. 2001;45(2):299–310. Manduca A, Lake D, Kruse S, Ehman R. Spatio‐temporal directional filtering for improved inversion of MR elastography images. Medical Image Analysis. 2003;7(4):465–73. Honarvar M, Sahebjavaher R, Sinkus R, Rohling R, Salcudean S. Curl‐based finite element reconstruction of the Shear Modulus without assuming local homogeneity: Time harmonic case. IEEE Transactions on Medical Imaging. 2013;32(12):2189–99. Xu H, Othman SF, Hong L, Peptan IA, Magin RL. Magnetic resonance microscopy for monitoring osteogenesis in tissue‐engineered construct in vitro. Physics in Medicine and Biology. 2006;51(3):719–32. Curtis ET, Zhang S, Khalilzad‐Sharghi V, Boulet T, Othman SF. Magnetic resonance elastography methodology for the evaluation of tissue engineered construct growth. Journal of Visualized Experiments 2012;(60):pii: 3618. Othman SF, Stosich MS, Xu H, Marion NW, Mao JJ, Magin RL. Monitoring adipogenesis in tissue‐engineered fat in vitro using microscopic magnetic resonance elastography. International Society for Magnetic Resonance in Medicine, 14th Scientific Meeting, Seattle, WA, May 2006. Othman SF, Curtis ET, Plautz SA, Pannier AK, Butler SD, Xu H. MR elastography monitoring of tissue‐engineered constructs. NMR in Biomedicine. 2012;25(3):452–63. Othman SF, Curtis ET, Xu H. In vivo Magnetic Resonance Elastography of mesenchymally derived constructs. IEEE International Symposium on IT in Medicine & Education. 2011:1: 233, 621–4, Guangzhou, China (2011). Khalilzad‐Sharghi V, Han Z, Xu H, Othman SF. MR elastography for evaluating regeneration of tissue‐engineered cartilage in an ectopic mouse model. Magnetic Resonance in Medicine. 2015;75(3):1209–17.

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6 Finite‐Element Method in MR Elastography Application in Tissue Engineering Yifei Liu1 and Thomas J. Royston2,1 1

Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL, USA Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA Corresponding authors emails: [email protected]; [email protected]

2

6.1 ­Introduction When solving engineering problems described by differential equations, such as stress analysis, heat transfer and deformation, closed form, or exact solutions cannot be directly obtained in many situations, especially when the structure or domain has complex loading and boundary conditions or complex geometry and material properties such as biological tissue and organ(s). The finite‐element method (FEM) is a computer‐aided numerical method to obtain an approximate solution by discretizing a continuous medium into meshed, interconnected, ­simple shaped elements. Therefore, the problem can be converted to solving ­subproblems on these simple shape elements. Also, the approximate solution of the problem can be obtained by summing up the solutions of these subproblems [1–3]. Nowadays, there are many commercial finite‐element analysis (FEA) ­software packages that can do various FEM analyses on user‐defined models, two‐dimensional (2D) or three‐dimensional (3D), with an interactive interface. Widely used FEA software packages include ANSYS (ANSYS, Inc., PA), COMSOL Multiphysics (Comsol, Inc., Stockholm, Sweden), ABAQUS (Dassault Systèmes Simulia Corp., MA), and ADINA (ADINA R&D, Inc., MA). FEA as known today was originally applied to structure analysis [4, 5], but now has been extended to solve multiple types of engineering problems that include heat conduction, fluid dynamics, and electric and magnetic fields [6]. FEA also has broad applications in tissue engineering. For example, in bone tissue engineering, integrating FEA and mechanobiology can be used to design temporary biomimetic scaffolds [7, 8]. In this application, the structural response, such as Magnetic Resonance Imaging in Tissue Engineering, First Edition. Edited by Mrignayani Kotecha, Richard L. Magin, and Jeremy J. Mao. © 2017 John Wiley & Sons, Inc. Published 2017 by John Wiley & Sons, Inc.

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stress and strain, within the scaffold structure and mesenchymal tissue can be analyzed with the FEM; consequently, the stimulus acting on the regenerated tissue can be determined. This helps identify the optimal parameters for best bone scaffold performance [7]. FEA can also be used in vascular tissue engineering. A multiscale mechanobiological modeling with FEM can be used to investigate the dynamics of vascular smooth muscle cell (VSMC) growth in vascular engineering scaffolds, and determine the role of scaffold compliance and loading in the development of intimal hyperplasia (IH) [8]. For the application on magnetic resonance elastography (MRE) in tissue engineering, FEM is typically used in several ways: (i) as a verification tool in inversion algorithm development; (ii) as an inversion algorithm to obtain the stiffness map; and (iii) as a designing tool in MRE actuation setup development, by doing harmonic analysis on simplified or 3D reconstructed models.

6.2 ­FEA in MRE Inversion Algorithm Verification In MRE, magnetic resonance (MR) phase images are acquired directly at certain phase offsets between the mechanical excitation and motion‐sensitizing gradient (MSG) [9]. Wave images, which show the wave pattern at different phase steps on the acquisition slice plane under the external harmonic excitation, can be obtained by applying simple Boolean operations on the phase images. However, converting the wave images to stiffness maps requires an inversion algorithm. There are several popular inversion algorithms in MRE that are adopted by most researchers. One is local frequency estimation (LFE) [10], which estimates the local wave numbers and then calculates the shear stiffness by using the phase speed equation in the viscoelastic medium. Another popular algorithm is algebraic inversion of differential equation (AIDE) [9] or algebraic Helmholtz inversion (AHI) [11], which derives the relation between the stiffness and displacement from the wave equation directly by making certain assumptions. Meanwhile, due to the noise in acquired wave images, spatial filters and directional filters are usually applied before the inversion [12]. Since in MRE, the shear stiffness is obtained indirectly from the wave images, the estimated stiffness values can be affected by the selected filter(s) and the overall accuracy of the reconstruction algorithm. Therefore, a verification tool is necessary to evaluate the fidelity of the filter approach and the accuracy of the inversion algorithm, and FEM has been adopted for the evaluation of several inversion algorithms, as described, for example, in Refs. [13, 14]. Figure  6.1 shows an example of using FEA to verify and compare two 2D inversion algorithms: the Helmholtz inversion algorithm and a 2D effective stiffness estimation algorithm [15], which is the weighted summation of the stiffness maps obtained from Helmholtz inversion algorithm using the motion encoded from the three directions. The model is described in Figure  6.1a.

6.2 ­FEA in MRE Inversion Algorithm Verificatio

(a)

(b)

Medium #1: μ = 11.8 kPa

U = Uaexp( jωt), ω = 2πf, f = 80 Hz

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Medium #2: μ = 7.4 kPa

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Figure 6.1  Example of using FEA to verify the inversion algorithm in MRE. (a) The geometry of the model is that of a fluid‐filled spherical shell embedded in a stiffer medium, and a solid spherical medium with the same density as the shell embedded in the medium for comparison. (b) The wave pattern of the model under a horizontal 80‐Hz harmonic excitation. (c) The stiffness map obtained from the regular Helmholtz inversion algorithm. (d) The stiffness map obtained from an effective stiffness estimation algorithm. (See insert for color representation of this figure.)

In this model, a fluid‐filled spherical shell medium with the shear stiffness of 7.4 kPa is embedded in a stiffer medium (shear stiffness is 11.8 kPa). For comparison purpose, a solid spherical medium with the same size of the inner boundary of the shell and the same material properties as the shell medium (shear stiffness of 7.4 kPa) is designed to be embedded in the medium as well. A harmonic excitation at 80 Hz is applied horizontally on the top surface of the model, and the bottom surface of the model is restricted in all directions. The motivation of this simulation is to find a more appropriate inversion algorithm for single‐slice MRE on a left ventricle (LV) mimicking geometry for cardiac MRE implementation. Figure 6.1b shows the wave pattern at the center slice due to external excitation. Figure 6.1c shows the stiffness map obtained from a regular 2D Helmholtz inversion algorithm and Figure 6.1d shows the stiffness map obtained from the 2D effective stiffness estimation algorithm. Comparing the results in Figure 6.1c and d, it can be observed that the effective stiffness estimation outperforms the Helmholtz inversion algorithm, especially at the area around the shell geometry. Both algorithms identified the shell and the solid medium. However, it can still be observed that the stiffness of the shell is slightly underestimated

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compared to the solid geometry. The estimated stiffness at the boundary of the model is much lower than what is defined in the FEM, and the estimated stiffness map does not have a homogenous distribution. Therefore, when using MRE to estimate stiffness, the values need to be averaged over a relatively large region of interest (ROI) to obtain a fair estimation. This example shows the capability of using FEM to verify the inversion algorithm in MRE with a simple 3D model. Meanwhile, it can also reveal some limitations of using the inversion algorithm, and can thus help the researchers to choose and use the inversion algorithm more wisely.

6.3 ­FEM in Stiffness Estimation from MRE Data In MRE, FEM can not only verify the inversion algorithm, but can also serve as an inversion method [16]. One example of using FEM to estimate the stiffness is a study in eye MRE from the group in Mayo Clinic. In this study, a 2D axisymmetric model was reconstructed from an MR magnitude image of a bovine globe. Also, an axisymmetric frequency‐response analysis was performed in COMSOL Multiphysics (Comsol, Inc., Stockholm, Sweden). The finite‐element model was solved by iteratively changing the Young’s moduli of the cornea and sclera, until it produced a wavefield that was comparable to the wave image from the MRE experiment [17]. Another more mathematical example using FEM as an inversion method in MRE is the so‐called “overlapping subzone technique,” which is developed by Dr. Van Houten et al. [18–20]. It is inconvenient to involve in a commercial FEM software in this case, and the FEM calculation has to be written in the inversion code. This technique considers the entire domain as a union of multiple “subzones.” An initial estimate of the Young’s modulus was defined over the entire space and a global displacement field is calculated from the initial property definition. A square‐root error metric of this global displacement and the displacement measured from the MRE data can then be calculated. The decreasing squared error metric determines the element centroid of subzones. The stiffness of each subzone can be estimated in a hierarchical order by progressively minimize the difference function between the measured displacement from MRE data and computed displacement from the mathematical finite‐element model of the subzone [19–21]. New subzones were generated from the element centroid in the error metric and the stiffness of the subzone is calculated in the same approach until every element has been visited. A global displacement is then recalculated with the updated stiffness, a new error metric is generated and the same procedure repeats until every element has been iterated a minimum number of times. This approach has an impressive result in both 2D and 3D in vivo and in vitro MRE data; but, the calculation time, especially for 3D data, maybe an issue.

6.4 ­FEA in Experimental Validation in Tissue Engineering Applicatio

6.4 ­FEA in Experimental Validation in Tissue Engineering Application The design of a mechanical actuation setup is one of the most important components in a successful MRE experiment. Without a valid mechanical actuation, the targeted tissue cannot be stimulated effectively and will result in an inaccurate viscoelasticity estimation. On the other hand, validation of the actuation setup design in MRE involves hours of MRE scan that is also costly. It is especially true in novel studies in small animal MRE applications. Most small animal MRE experiments are done with preclinical MR scanners; the small dimension of the radiofrequency (RF) coil for preclinical MR scanners makes the space for MRE experiment setup extremely limited. On the other hand, excitation of deep organs in small animals needs a relatively stronger driver; both the dimension and the location of the driver affect the result of the excitation. Hence, doing simulations based on FEMs with different driving methods, including in terms of location and frequency, on simplified and/or animal models is an effective and economic approach to aid the actuation design. Also, establishing and continuously improving a 3D animal model are beneficial for testing the tissue response to different stimuli in tissue engineering. Figures  6.2 and 6.3 show an example of using FEA simulations for MRE actuation design [22]. In this example, a 3D mouse model of the thorax region, shown in Figure 6.2a, was reconstructed from an in vivo multislice MRI scan of the mouse heart and a postmortem micro‐CT scan of the same mouse. The material properties of each organ (e.g., fat, rib cage, lung, myocardium, blood, and bone) were defined from analogical estimation from measurement of other animal models such as porcine, or empirical estimation from tissue mimicking phantom measurement from the literature [23–29]. Various simulations of harmonic analysis with excitation at 400 Hz and the same excitation amplitude but with different excitation area and directions were conducted on this model. The purpose of these simulations was to aid the design of an efficient actuation in mouse cardiac MRE implementation. Thus, the location of the excitation was chosen to be close to the LV. Also, as a cardiac MRI scan standard practice, a short‐axis slice scan should be considered for all scans in order to observe the dilation of the LV in the radial direction. Hence, a short‐axis slice was also selected in this example as shown in Figure 6.2b. Simulations of three excitation types were done in COMSOL Multiphysics (Comsol, Inc., Stockholm, Sweden) in this example. The first excitation was applied in the direction parallel to the mouse body on a 3‐mm diameter area. This is to mimic the excitation design of using a piezo stack actuator as a driver. Due to the limitation of space in the RF coil, the piezo stack actuator can be placed in parallel to the mouse body and drive the mouse chest with a long rod. In this case, the excitation direction would be parallel with the mouse body,

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(a) Shoulder and arms Rib cage

Lung

Right ventricle

torso

Left ventricle Myocardium

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Short-axis plane

Figure 6.2  An example of using FEA for actuation design in MRE. (a) A 3D model of the thorax region of a mouse. (b) The short‐axis slice selection plan in the model. Liu [22].

and the excitation area would be the diameter of the rod. The second excitation method was applied on the same 3‐mm diameter area but in the direction normal to the excitation plane. This is to mimic the excitation design of using a cymbal actuator or thin piezo component [30]. In this case, the cymbal actuator or a thin piezo component will be placed on the top of the chest, and in this case the excitation direction will be normal to the mouse body. Because of the curvature of the mouse chest, the contact area of the chest and the actuator would be approximately 3 mm. The third excitation method was still in the direction normal to the excitation plane, but on a 6‐mm diameter area at the same location. This is to mimic the actuation design of using an acoustic driver system that has a tube tip in this dimension to cover the entire heart area [15]. In this case, the tube tip has an opening of 6 mm, and the design of the tube tip opening end has a curvature similar to the mouse chest, so the excitation area can reach to 6 mm in diameter. Figure  6.3 shows the wave amplitude result from these three simulations. The top row (Figure 6.3a–c) shows the wave amplitude result on the 3D model

(a)

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freq(1) = 400 Surface: Wave amplitude (μm)

freq(1) = 400 Surface: Wave amplitude (μm)

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Figure 6.3  Displacement amplitude results for three simulations. The top row is the displacement amplitude shown on the 3D models of (a) harmonic excitation on a 3‐mm diameter area in the vertical direction, (b) harmonic excitation on a 3‐mm diameter area in the direction normal to the excitation plane, and (c) harmonic excitation on a 6‐mm diameter area in the direction normal to the excitation plane. The bottom row is the displacement amplitude map on the short‐axis slice plane of (d) harmonic excitation on a 3‐mm diameter area in the vertical direction, (e) harmonic excitation on a 3‐mm area in the direction normal to the excitation plane, and (f ) harmonic excitation on a 6‐mm are in the direction normal to the excitation plane. (See insert for color representation of this figure.)

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for the three cases to provide a visual depiction of how widely the wave can spread. The excitation area is marked with the solid line on the mouse body. The bottom row (Figure 6.3d–f ) shows the wave amplitude result on the short‐ axis slice plane to show how deep the wave can propagate. It can be easily observed that under the same input excitation amplitude, excitation method 3 (Figure 6.3c and f ), which has the largest excitation area, has the best result. Excitation method 1 (Figure 6.3a and d), which has an excitation direction parallel to the mouse body, has the worst result. On the other hand, although the excitation diameter was only double in method 3 (Figure 6.3c and f ) compare to method 2 (Figure 6.3b and e), the wave penetration was much deeper. With the aid of these simulations, the excitation 3 was chosen for the mouse cardiac MRE implementation, which can avoid spending too much time and cost on testing the other two excitation methods experimentally. Hence, using FEA enables the possibility for doing virtual MRE experiments by manipulating different excitation designs, including geometry, location, frequency, excitation amplitude, and so on. This is helpful in making a decision on design layout for the in vivo MRE experiment. However, there are still limitations in using FEA for actuation design. The accuracy of the simulation result is highly dependent on the definition of the material properties for each organ. There are few publications providing in vivo material property values for certain organs in animal models. To the best of the authors’ knowledge, no publications provide a systematic measurement of material (mechanical) properties for every organ, even for the most popular animal models such as murine, canine, and porcine. The organ material properties that have no direct reference can only be defined either by empirical estimation or analogous to references about other animal models or even human organ properties. On the other hand, the anatomical geometry and stiffness also change when the a­ nimal changes the position, such as from supine to upright position, or the muscle condition changes from relaxed to forced. All of these changes will result in change in the simulations. Thus, FEA on animal‐related MRE experiment design can provide conceptual information, but cannot be fully and accurately comparable with the experimental result. It can be helpful in reducing the MRE experiment needs during the actuation design stage, but the actual MRE experiments are still needed for the final test and adjustment.

6.5 ­Conclusions and Discussion In this chapter, applications of the FEM in MRE of tissue engineering were introduced. Using FEM as a verification tool for the MRE inversion algorithm development has been widely adopted. However, most of these verifications were done on relatively simple geometry. When applying the inversion algorithms on in vivo implementations, the complicated boundary conditions and

 ­Reference

intrinsic motion of the organs make the wave propagation complicated. More simulations might be needed to apply inversion algorithms on human or animal models to verify their accuracy. Using FEM as the stiffness estimation method can provide a more accurate result, and has been adopted in several studies. But, it is limited in the performance of the computer for solving iterative simulations and/or calculations, and also requires a longer computation time in solving the problem. The usage of FEM as an actuation design aid has progressed slowly due to (i) limited availability of ready models and (ii) lack of information on accurate material properties of organs for each animal model or human. The continuous improvement of animal models with more accurate material properties is necessary, which would not only be useful in actuation design in MRE, but also benefit application of FEM in other studies in tissue engineering.

­Acknowledgment This work was supported in part by the National Institutes of Health (Grant # EB012142).

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7 In Vivo EPR Oxygen Imaging: A Case for Tissue Engineering Boris Epel1,*, Mrignayani Kotecha2, and Howard J. Halpern1 1

Department of Radiation and Cellular Oncology, Center for EPR In Vivo Physiology, The University of Chicago, Chicago, IL, USA 2 Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA * Corresponding author email: [email protected]

7.1 ­Introduction The field of tissue engineering is advancing at a rapid pace. The discovery of new biomaterials, the advanced research in stem cell biology, scaffold design, and tissue growth strategies all brings us closer to the goal of regenerating human tissues and organs [1]. However, most engineered tissues created to date do not meet functionality criteria. Many parameters of tissue growth need to be optimized to achieve the full potential of the processes at cellular and tissue levels. These include optimized nutrient transport and utilization, optimum scaffold design, optimum and uniform matrix formation throughout the tissue depth, and optimum cell attachment, differentiation, and proliferation. One parameter that affects all of these criteria is the optimum oxygen tension throughout the tissue. It is hard to overestimate the effect of oxygen on cell well‐being and function. The lack of oxygen (hypoxia) creates a potentially dangerous environment for most cell types that affects cell physiology and growth; in some cases, it leads to the modification of cell phenotype. Diffusion through culture medium and tissue is the bottleneck for oxygen transport in vitro, leading to hypoxic regions in the center and limiting the viable tissue thickness. For making clinically relevant tissue‐engineered grafts, the problem of sufficient oxygen delivery in entire sample should be addressed.

Magnetic Resonance Imaging in Tissue Engineering, First Edition. Edited by Mrignayani Kotecha, Richard L. Magin, and Jeremy J. Mao. © 2017 John Wiley & Sons, Inc. Published 2017 by John Wiley & Sons, Inc.

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Oxygen is recognized as a model nutrient because of its importance in cell survival [2]. It has been shown that highly vascular engineered tissues such as bone, liver, and cardiac muscle thrive on normal oxygen concentrations, while the avascular engineered tissues such as cartilage thrive at low oxygen concentration [3, 4]. It has also been shown that the cell viability decreases with decreased oxygen concentration, for example, as a function of tissue depth in engineered cardiac tissue [5]. Multiple strategies are designed to overcome the limitations of insufficient oxygen transport in tissue engineering grafts. These include designing scaffolds with artificial microvasculature, oxygen carriers, and hyperbaric oxygen chambers [6, 7]. Some studies have focused on optimum scaffold design for proper oxygen transport [8]. All of these approaches will benefit from the knowledge of local oxygen in an engineered graft. On the other hand, a better understanding of the roles of hyperoxia or hypoxia in tissue growth is needed to understand the role of oxygen in cell survival, viability, and differentiation. A technique that can map oxygen in vitro and in vivo in hourly, daily, weekly, or yearly basis will greatly enhance the efficiency of tissue‐engineered graft designed to solve the problem of organ or tissue damage. Despite the clear importance of the partial oxygen pressure (pO2) knowledge, there is no single pO2 measurement method that would satisfy all requirements. pO2 maps can be acquired using pulse oxymetry, electrodes, oxygen‐quenched luminescence, and 19F magnetic resonance imaging (MRI) [9]. Not all of these methods are quantitative and allow non invasive repetitive measurements. Phosphorescence quenching microscopy is quantitative and repetitive but is limited to the depths of few millimeters due to high tissue absorption at infrared frequencies [10, 11]. EPR oxygen imaging is a noninvasive oxygen mapping method similar to MRI. EPR manipulates unpaired electron spins with the help of uniform static magnetic field and magnetic fields ­gradients to generate images of spin magnetization (see Chapter 2). If the relaxation of the spin probe is oxygen dependent, then electron paramagnetic resonance oxygen imaging (EPROI) can provide a map of local oxygen pressure at the site of inquiry. EPROI has been widely used in vivo for obtaining tumor oxygenation in solid tumors [12]. Figure 7.1 shows an ­oxygen map obtained in fibrosarcoma tumor grown on a mouse leg. Ellis et  al. demonstrated accurate local oxygen maps in a tissue‐engineered ­cartilage generated using chondrocytes grown in hollow fiber bioreactor for the period of 4 weeks [13]. In the following sections, we provide history and principles of EPROI, as well as details on EPROI instrumentation ­followed by few examples of EPROI used in tissue engineering for checking the viability of scaffold design and in vivo oxygen maps for tumor tissue engineering.

7.2 ­History of EPRO

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Figure 7.1  Three‐dimensional oxygen map of fibrosarcoma tumor and tumor bearing leg. The tumor outline, determined from a registered MR image, is shown in red. The image was acquired using 250‐MHz pulse EPR oxygen imager. (See insert for color representation of this figure.)

7.2 ­History of EPROI After the discovery of the technique for obtaining spatial images using nuclear magnetic resonance by Paul Lauterbur [14], it did not take long to find the ways for applying the magnetic field gradients for obtaining EPR images as well. Early works on EPR imaging date back to late 1970s when Hoch and Day [15] and Karthe and Wehrsdorfer [16] independently obtained EPR images of diamond and DPPH (2,2‐diphenyl‐1‐picrylhydrazyl) particles. The chance to image biological samples using EPR imaging came in 1980s with Berliner and Fujii [17] who reported images of celery stems soaked in nitroxide. Development of spectral–spatial imaging in the late 1980s [18, 19] radically enhanced the arsenal of EPR imaging methods and allowed to determine multiple spin probe parameters from EPR line shape. Unlike conventional MRI, the most common EPR imaging methodology used before 2000 was continuous wave (CW) methodology. A number of studies, however, prepared the advent of pulse imaging technology. Milov et al. [20] and Eaton et al. [21] demonstrated the application of electron spin echo for imaging. In the middle 1990s this advancement resulted in the development of three‐dimensional (3D) spin‐echo phase relaxation [22] and spin–lattice relaxation [23] imaging. Based on solid‐state MRI methodology, Subramanian

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et al. introduced an alternative methodology, single‐point imaging (SPI) that was applied for phase relaxation imaging [24]. The first EPR detection of oxygen concentration using a nitroxide spin probe was reported by Backer et al. [25] and Popp et al. [26]. Later studies by Swartz and coworkers [27, 28] extensively cover various classes of spin probes and described the relation between pO2 and spin probe relaxation. In the 1990s, a few groups pioneered multidimensional CW imaging on rodents in vivo and ex vivo, enabling repeated measurements of oxygen concentrations in ex vivo tissues [29, 30] and in living animals [31]. With the development of narrow‐line trityl spin probes [32, 33], the focus of preclinical oximetric imaging shifted to pulse methodologies [22, 23, 34].

7.3 ­Principles of EPR Imaging EPR observes an interaction of an unpaired electron with the magnetic field [35, 36]. An electron has a spin S = 1/2 and thus a magnetic moment. Quantum mechanics postulates that in the presence of a constant magnetic field, B0, the magnetic moment of electron can take only two orientations—parallel or antiparallel, to the magnetic field. An oscillating magnetic field with frequency ω0 calculated according to Equation (7.1) causes a transition between these two states. (7.1) e B0 0 This results in the resonant absorption of magnetic field and can be detected. Here, γe = gμB/ℏ is the electron gyromagnetic ratio, equal to 1.76 × 1011 s−1 T−1 for the free electron. The experiment in which continuous oscillating magnetic field with frequency ω0 is applied and the change in sample absorption is detected as a function of ω0 or B0 is called continuous wave (CW) EPR. The modification of this experiment with one or multiple short pulses of oscillating magnetic field applied to the sample is called pulse EPR. In this experiment, the macroscopic magnetic moment induced in a sample is observed after the pulse. Because signal evolution from all electron spins is observed in time, this method is also called time‐domain EPR. Both CW and pulse EPR methods are actively used today and have their respective application areas. For imaging, the spatial position of paramagnetic species is encoded using static magnetic field gradients, G. The gradient alters the amplitude of the constant magnetic field at position r in a linear fashion (7.2) B B0 G r The direction of the magnetic field does not change. The direction of the gradient can be varied in space. Most imagers use three gradient coils that create the gradients in principal directions. The superposition of gradients from

7.3 ­Principles of EPR Imagin B = B0+ G · r y G B0 x

G

B1

G G=0 B2

B1

B2

B0

Figure 7.2  Influence of the magnetic field gradient G on EPR spectrum. Schematic EPR spectrum is shown in the bottom‐right corner. Three orientations of G in the axial plane of object are shown. Benjamin et al. [37]. Reproduced with the permission of Elsevier.

these coils creates the sought‐for arbitrary gradient. In contrast to conventional water proton MRI, six orders faster relaxation rates of electrons prohibit the use of pulsed gradients in EPR imaging except for microscopic samples. The formation of the EPR image is explained in Figure 7.2. For simplicity, we consider here a 2D problem. The phantom in the figure consists of the cross section of two vertical tubes filled with an EPR spin probe. The EPR spectrum of this probe is shown in the bottom‐right corner. Depending upon the orientation of the magnetic field gradient, different spectra will be observed. For example, if the gradient field reduces the permanent magnetic field, then the resonance will be observed at a higher magnetic field and vice versa (see spectra at the bottom and bottom‐left). This results in the appearance of two peaks in the spectra. In the case when the gradient field does not alter the main magnetic field for both tubes, a single line spectrum will be observed. This method of image formation by obtaining multiple low‐dimensional views of an object is called tomography and the spectra are called the projections. It can be shown that EPR spectra recorded with applied gradients can be described using the Radon transformation [37]. There are numerous ways to perform the inverse Radon transformation to recover the image of the object from projections obtained with a full angular set of gradients; the most ­common implementation of the transformation is filtered backprojection or FBP [38]. The images that exhibit the distribution of spin probe are called spatial. Medical images are typically three-dimensional; however, there are applications for the images with the lower dimensionalities. Images that map spatially

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resolved spinprobe spectra are called spectral–spatial images or, sometimes, 4D images. The shape of EPR spectrum can carry information about the spin probe state and its environment. Spin probes for reporting viscosity, temperature, pH, oxygen concentration, and redox potential are available and are used for imaging. The third image type that became important with the advent of pulse EPR is parametric image. It is produced from spatial images extended to an additional dimension by varying some parameters of the pulse sequence.

7.4 ­EPR Oxymetry The oxygen molecule is a diradical with two unpaired electrons in triplet state that defines its paramagnetic behavior and extremely fast relaxation. In a solution, oxygen molecules collide with introduced spin probe molecules, whose spectral and solubility properties have been optimized for in vivo application. During these collisions, the electrons of oxygen and spin probes participate in a process called Heisenberg exchange, which results in electrons of spin probe experiencing the environment of the oxygen electrons and relaxation rate enhancement [39, 40]. The Smoluchowski diffusion equation predicts a linear relation between the ­oxygen pressure (pO2) and the collision rate, which in turn is linearly related to the relaxation rate. The linear relation between pO2 and relaxation rates is validated for multiple free radicals [41] and is the basis of EPR oxymetry. Heisenberg spin exchange acts similarly on the spin probe’s transverse and longitudinal relaxation [40, 42]. Importantly, other relaxation processes affect transverse and longitudinal relaxation differently. The most remarkable difference is experimentally observed for spin probe self‐relaxation due to collision between spin probes. The self‐ relaxation is dependent on the number of collisions per unit time, and, therefore, on the concentration of spin probe. One of the important properties of the spin exchange interaction is that the total energy of the interacting spin pair does not change. Thus, the exchange between spin probes does not change the magnetization, resulting in a low concentration dependence of the longitudinal relaxation [40, 43]. On the contrary, a part of the transverse relaxation depends on the spin phase decoherence that is strongly impacted during collision leading to much stronger concentration dependence as shown in Figure 7.3. Therefore, for high‐ accuracy oxygen measurement methods, measuring longitudinal relaxation is preferable for accurate oxygen measurement. For a number of spin probes, such as trityl or lithium‐phthalocyanine (LiPC) radicals, the pO2 precision exceeding 1 Torr was determined [23]. This precision, absolute accuracy, and relative noninvasiveness are the unique features of EPR oxymetry in comparison to other noninvasive methods such as BOLD MRI, which reports blood hemoglobin desaturation and positron emission tomography (PET) from 18F substituted nitroimidazoles which reports reductive retention in tissue regions with low pO2 [9, 44].

7.5 ­EPROI Instrumentation and Methodolog

(b)

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Figure 7.3  Relaxation rates of Oxo63 trityl dissolved in saline at 37°C. (a) Concentration‐ corrected dependence of relaxation rates on pO2. (b) Concentration dependences of spin–spin (R2) and spin–lattice (R1) relaxation rates. Adapted from Epel and Halpern [12]. Reproduced with the permission of John Wiley & Sons.

7.5 ­EPROI Instrumentation and Methodology Major components of EPR imaging technology are radiofrequency (RF) or microwave frequency electronics, magnets and magnetic field gradients, power supplies, resonators, computer control software and digital electronics, data processing, and image reconstruction software. 7.5.1  EPR Frequency

The sensitivity of the EPRI instrument is largely defined by its operational frequency ω0. The signal grows as a square of ω0, the sample noise grows approximately as ω0 [45], whereas the penetration of RF into biological sample decreases approximately with the square root of ω0 [46]. These dependencies define optimal frequencies for EPR measurements as a function of object size. Most of the current EPR in vivo imagers operate in one of the three frequency ranges. The 250–350‐MHz (9.6–13.4 mT) instruments are designed for large rodents, rabbits, and could be used for human subjects. The 550–750 MHz (21.1–28.8 mT) instruments are optimal for full body mice imaging, and 1–1.2 GHz (~42 mT) or L‐band imagers primarily target mice and their peripheral anatomy. Only L‐band imagers are available commercially, whereas most of the other instruments are custom built. 7.5.2 Resonators

The function of EPR resonator is to create a magnetic field with frequency ω0 and detect the EPR signals from the spins either in the form of macroscopic magnetization (pulse EPR or transmission resonators) or in the form of

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7  In Vivo EPR Oxygen Imaging: A Case for Tissue Engineering

(a)

(b) Loop and gap of resonator Resonator coupling interface

AGR LGR

Figure 7.4  EPR resonators. (a) Schematic drawing of the conductive surfaces of an LGR and 250‐MHz split‐top EPR LGR that allows in situ irradiation with mouse leg installed and upper part removed [47]. (b) 250‐MHz Alderman‐Grant bimodal resonator [48]. Sundramoorthy et al. [48]. Reproduced with the permission of Elsevier.

resonator mismatch (CW EPR and reflection resonators). The signals are then amplified, detected, and typically down converter to zero frequency. At the frequencies used for EPR imaging (250 MHz to 1.2 GHz), the most common resonator designs are based on the loop‐gap resonator (LGR) structure, see Figure 7.4. The inductive loop is formed by wrapping a cylindrical, conductive sheet around the object. The loop is firmed with a gap separating the edges of the sheet. The gap is typically extended to achieve sufficient capacity to resonate the structure; external capacitive elements can also be used. LGRs are simple in construction and provide excellent access to the sample. Despite moderate magnetic field inhomogeneity, highest resonator efficiency makes LGR a workhorse of the EPR imagers. Typical use of these resonators is in reflection mode where the application of external power and signal reception is done through the same resonator port. Transmission‐mode resonators with separated signal paths for excitation and detection can offer better isolation of weak EPR signals from the excitation power at the expense of more complicated structure [48]. 7.5.3 Magnets

Low‐frequency EPR imagers utilize air core electromagnets at the fields below 40 mT, whereas higher field magnets have iron core. There is an increasing use of permanent magnet, especially for the intermediate fields. Permanent magnets considerably reduce power requirements of the imager but have lower field homogeneity and much worse thermal stability. The design of gradient coils is similar to MRI coil design [49]. Owing to the application of the static magnetic field gradients, the EPR coils have much more relaxed inductance

7.5 ­EPROI Instrumentation and Methodolog

(a)

(b)

Figure 7.5  (a) 250‐MHz (8.9 mT) imaging system built in the University of Chicago that utilizes air core magnet. (b) Commercial iron core L‐band (1.2 GHz, 43 mT) Bruker E540 System.

requirements but should withstand higher power deposition. Gradients fields with the strengths of up to 1 T/m are used. 7.5.4  EPR Imagers

Figure 7.5 shows the 250‐MHz imaging system built in the University of Chicago and the commercial L‐band Bruker E540 imager. In both cases, imagers have small footprint of the general laboratory size equipment and do not require specialized environment. The commercial (Bruker X‐EPR) and free software (EPR‐ IT, http://epri.uchicago.edu) for image reconstruction are available. Two methodologies commonly used for EPR imaging are CW [50] and pulse EPR [12]. Both use slightly different equipment designs and are applied to different problems. CW EPR is the most versatile and can be applied to all kinds of spin probes, including those used for pH and thiols measurement, while pulse EPR applications are limited to the spin probes with slow relaxation (see Section  7.6). However, whenever applicable, pulse EPR imaging has demonstrated a remarkable advantage over CW imaging both in terms of accuracy and acquisition time. In particular, this is true for oxygen imaging [51]. Pulse imaging utilizes a variety of pulse sequences that enable parametric imaging [52]. Figure 7.6 shows an example of pulse sequence for spin–lattice relaxation imaging (IRESE) used in oxymetry. The acquisition sequence consists of three pulses. The first π (180°) pulse inverts the magnetization. During the interval after first pulse (T), the magnetization returns to its equilibrium state. A spin echo is recorded after the two‐pulse detection π/2‐τ‐π‐τ‐echo sequence. Fourier transformation of the echo shape gives a projection. The separate 3D images with different T‐delay values in IRESE sequence are obtained (Figure 7.6a). Then, for each voxel of these images, the signal evolution is fitted to the exponential recovery to determine the spin–lattice relaxation time.

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7  In Vivo EPR Oxygen Imaging: A Case for Tissue Engineering (a)

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Figure 7.6  Acquisition of parametric pulse EPR images. Inversion recovery sequence for spin–lattice relaxation imaging (IRESE). (a) A scheme of electron spin echo shapes obtained with different delays between inversion pulse and detection part of the sequence, T. (b) Fitting image voxel intensity to a function of T to obtain spin–lattice relaxation time, TSLR. For complete echo inversion a = 2. Adapted from Epel and Halpern [52]. Reproduced with the permission of Elsevier.

7.6 ­Spin Probes for Pulse EPR Oxymetry Highly concentrated electron paramagnetic centers such as free radicals are not frequent in nature and even less frequent in living bodies. Endogenous to human body paramagnetic species include hemoglobin, various metal centers of enzymes, diffusible radicals, and molecular oxygen. Most of these paramagnetic centers have very low concentration in tissues or short relaxation times and, therefore, cannot be detected in vivo. At present, exogenous spin probes are the only practical reporters, and appropriate spin probes are the key to successful in vivo imaging. There are two large classes of spin probes used in EPR oxymetry: soluble free radicals and insoluble paramagnetic particles [53]. Both classes have biologically compatible probes capable of measuring in living organism fluids. Soluble radicals are more appropriate for noninvasive imaging. For live subjects, they can be administered by intravenous, intraperitoneal, or intra‐arterial injections. In cell or in vitro tissue cultures, the soluble spin probes are distributed by media flow or diffusion. Since spin probes are found to distribute rather uniformly in the soft tissues with the exception of brain, they allow whole body oxygen measurements. For decreasing of the amount of spin probe introduced to the subject, more localized delivery such as direct injection or intra‐arterial injection can be practiced. Easy accessibility of tissues by soluble spin probers, however, has a downside. Soluble probes quite rapidly (10–30 min) clear from the tissues and therefore require reinjection or continuous infusion for prolonged continuous measurements.

7.7 ­Image Registratio

The success of oxymetry in the last decade is strongly linked to triarylmethyl radicals or trityls (GE Healthcare, Little Chalfont, Buckinghamshire, UK) ­possessing a narrow single EPR line [32, 54]. Trityls are tri‐salts and have excellent solubility. The commonly used trityls for in vivo oxygen imaging are methyl‐tris [8‐carboxy‐2,2,6,6‐tetrakis[2‐hydroxyethyl]benzo[1,2‐d:4,5‐d′]bis[1,3] dithiol‐4‐yl]‐trisodium salt, OX063 (16 μT p–p), and its partially deuterated form (8 μT p–p). The relaxation rates of trityls are linearly dependent on the oxygen partial pressure, pO2 [32], as shown in Figure 7.3. These spin probes are distributed in the extracellular fluid compartment [55]. In the blood stream of a mouse, the clearance halftime of these probes is approximately 9–10 min, whereas in tumors they remain and provide strong signals for about 40–50 min [56]. The lethal dose (LD50) of OX063 is large, being 8 mmol/kg that allows high dose injections without compromising the safety [57]. Insoluble paramagnetic particles possess limited mobility. They can be incorporated into tissue during the growth stage. Application of protective coating allows increasing their stability to years that allows long‐term repetitive measurements in a tissue of interest. Most common particulates used for oxymetry are derivative of lithium phthalocyanine [58] such as octa‐n‐butoxy‐naphthalocyanine [59]. Very high concentration of spins ensures high EPR signals and strong relaxation rate pO2 dependence delivers very high pO2 measurement precision. However, even simple materials as activated charcoal or India ink can deliver EPR signals compatible with reliable in vivo pO2 measurements [60].

7.7 ­Image Registration EPR signal amplitude images do not exhibit contrast between different tissues as the OX063 spin probe has comparable perfusion to most soft tissues with the exemption of brain. For assignment of the oxygen maps to the anatomic features, an anatomic imaging modality such as MRI, CT, or ultrasound should be used. Since most of the imagers do not have the capabilities for the exact object placements, there is a need for postacquisition procedure that establishes the relation between image regions and image registration. Upon registration, any point in the coordinate system of one image can be translated into the coordinate system of another image. To introduce spatial anchoring into EPR images, fiducial tubes filled with EPR material are used [61]. The animals are immobilized with the help of the dental mold material, which is introduced in liquid form and quickly solidifies into rubber‐like material. This ensures a fixed relative position between ­animals and fiducials during animal relocation from one imager to another. Postacquisition of the fiducials in all image modalities are registered, which in turn register the images. After that, the results of segmentation can be transferred from anatomic image into EPR as shown in Figure 7.7.

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Fiducials Mouse leg EPR

50 40 30 20

pO2

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Figure 7.7  Example of MR and EPR image registration. The tumor area (brighter than the leg) is determined in MR image. After image registration, the tumor area is transferred from MR to EPR image for oxygen analysis. (See insert for color representation of this figure.)

7.8 ­Tissue Engineering Applications 7.8.1  EPROI in Scaffold Design

Biomaterials used as scaffold in tissue engineering provide necessary support for cell attachment, differentiation, and proliferation. They are chosen for their suitable mechanical properties related to the tissue being designed. Commonly used 3D natural biomaterials for tissue engineering purposes are collagen, alginate, fibrin, agar, etc. These materials provide excellent support for cell adhesion, proliferation, and differentiation; however, they are soft and in many cases not suitable for hard tissue engineering. On the other hand, synthetic polymers such as polylactic acid (PLA), polylactic‐co‐glycolic acid (PLGA), and polyethylene glycol diacrylate (PEGDA) offer greater tuning capability for mechanical properties, but in many cases do not support cell attachment as good as natural polymers. A common compromise is often a hybrid material made from both synthetic and natural polymer. The first step in assessing oxygen in an engineered tissue is to assess the spin probe penetration through the entire tissue depth. Figure 7.8a shows an exemplary spatial distribution image of OX063 in a scaffold prepared using PLGA

7.8 ­Tissue Engineering Application

(a)

Gradient PLGAhydrogel martix

EPR MRI Pore sizes 200–500 μm

1H

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Torr

pO2

0 –4 –8

Figure 7.8  (a) MRI and EPR spin probe distribution in bulky acellular PLGA scaffold in PBS (sample courtesy of Dr. Syam Nukavarapu). (b) 1‐mm‐thick acellular collagen gel (2 mg/ml) (The gel was provided by Dr. Michael Cho.) Registered CT, EPR spin probe distribution, and mock‐up oxygen image of the deoxygenated sample. (See insert for color representation of this figure.)

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microbeads [62]. The homogenous distribution of spin probe in entire sample suggests its suitability for oxygen assessment in vitro or in vivo. Figure  7.8b gives another example of such assessment on collagen I samples. This proves the accessibility of scaffolds to soluble EPR oxygen probes. 7.8.2  EPROI in Tissue Engineering

In a study where EPR oxygen imaging was used for deriving oxygen maps in tissue‐engineered cartilage, it was found that oxygen maps correctly predicted the change in oxygenation according to tissue growth condition [13]. In this study, chondrocytes were grown for 4 weeks in hollow fiber bioreactor with and without the addition of cyanide (a potent oxidative phosphorylation inhibitor). Oxygen maps were created using EPR oxygen imaging with a nitroxide spin probe. EPR oxygen maps followed the expected trajectory with and without the use of cyanide. Longitudinal oxygen studies are more common in cancer research. For example, Bratasz et  al. track oxygen deficiency (hypoxia) in growing tumors monitored between days 5 and 9 using EPR imaging [63]. These studies show the potential use of EPR oxygen imaging in assessing tissue oxygenation in vitro and in vivo.

7.9 ­Summary and Future Outlook In this chapter, we have demonstrated that EPR oxygen imaging is a powerful tool for oxygen mapping both in vitro and in vivo. The use of trityl spin probe allows the unambiguous and robust assignment of oxygen partial pressure distribution in live tissues. The current oxygen resolution of EPROI is better than 1 Torr and current spatial resolution of this method is 1 mm. The widespread use of EPR oxygen imaging in tissue engineering applications will greatly enhance the efficiency of TERM approaches. EPR oxymetry can be used to optimize the cell density and pore distribution of engineered grafts for stem cell therapies for diabetes, osteoarthritis, cardiovascular, or peripheral nerve regeneration. It can also be used for monitoring tissue viability after the implantation at preclinical stage. Currently, this technique is not available in clinical use that is limiting its application to humans. The availability of commercial oxygen imagers for tissue engineering laboratories may greatly enhance the control over the engineered tissue production and may standardize growth protocols for viable biomaterials and cell cultures.

­Acknowledgment B.E. and H.J.H. acknowledge the support provided by NIH grants P41 EB002034, R01 CA098575 and R50 CA211408.

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Part II Tissue‐Specific Applications of Magnetic Resonance Imaging in Tissue Engineering

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8 Tissue‐Engineered Grafts for Bone and Meniscus Regeneration and Their Assessment Using MRI Hanying Bai, Mo Chen, Yongxing Liu, Qimei Gong, Ling He, Juan Zhong, Guodong Yang, Jinxuan Zheng, Xuguang Nie, Yixiong Zhang, and Jeremy J. Mao* Center of Craniofacial Surgery, College of Dental Medicine, Columbia University Medical Center, New York, NY, USA * Corresponding author email: jmao@ columbia.edu

8.1 ­Overview of Tissue Engineering with MRI Tissue engineering, the science and engineering of creating functional tissues and organs for transplantation, integrates a variety of scientific and engineering disciplines to produce physiologic “replacement parts” for the development of viable substitutes to restore, maintain, or improve the function of human tissues [1–4]. In the success of tissue engineering, three‐dimensional (3D) scaffolds or grafts play an important role as extracellular matrices (ECMs) onto which cells were able to attach, grow, and form new tissues [1, 5, 6]. Tissue engineering has been widely applied in hard tissue (e.g., bone) defect treatment [7, 8]. The grafts or scaffolds are developed as a substitution to replace bone defects, and to stimulate osteoconduction and bone regeneration [5, 9, 10]. In some cases, such as tibial graphs, for example, the scaffold also has to take on the role of weight bearing for the connection of both broken parts [9, 11, 12]. Compared with bone healing, especially cortical bone healing, the meniscus healing is challenging and complicated. Meniscus is a complex tissue consisting of collagen fibers and proteoglycans (PGs) with gradient phenotypes of fibrocartilage and functions to provide congruence of the knee joint [5, 8, 13]. Meniscus is an avascular tissue, which limits its ability to heal spontaneously [13, 14]. Magnetic resonance imaging (MRI) is a noninvasive and nondestructive imaging technique for both visualizing biological tissues at the macro‐ and microarchitectural level and for quantifying the clinical outcome [7, 13, 15–18]. Magnetic Resonance Imaging in Tissue Engineering, First Edition. Edited by Mrignayani Kotecha, Richard L. Magin, and Jeremy J. Mao. © 2017 John Wiley & Sons, Inc. Published 2017 by John Wiley & Sons, Inc.

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MRI is important to the success of grafts or scaffolds made from tissue engineering [12]. The preclinical imaging, the postoperative monitoring, and outcome evaluation are performed using MRI [7, 16, 17, 19]. With the development of 3D printing technique applied in tissue engineering for the manufacture of implants, MRI became more significant for the creation of 3D model or representation of the implant [20, 21]. The medical or industrial software, such as Mimics, Solidworks, and AutoCAD, are utilized to create, modify, and optimize the digital format 3D models [1, 22]. The digital MRI files are the most convenient data source to determine the size, geometric parameters, shape, surface characteristics, and inside structure of the upcoming implants [23, 24]. In the treatment of bone trauma and defects, though X‐ray radiograph and X‐ray microtomography (micro‐CT) could provide the straightforward images of lacunae and bony growth in regeneration, the healing process and quality of other tissue are hard to detect [25]. MRI could not only provide the comprehensive information of the tissue growth, necrosis, and regeneration, but also has the advantage of being an ionizing radiation free imaging modality [16, 26, 27]. In the treatment of meniscus injury, MRI is significant in the characterization of both the tear pattern and tissue quality [28, 29]. In addition, MRI combined with other clinical invasive imaging such as second‐look arthroscopy could make quantitative outcome measurement of clinical results in meniscus repair and regeneration [30]. Furthermore, novel quantitative MRI sequences allow for objective, noninvasive assessment of tissue repair and regeneration at a biochemical and cellular level [13].

8.2 ­Assessment of Bone Regeneration by Tissue Engineering with MRI Bone is a dense connective tissue with a strong calcified outer layer, cortical bone, and soft inner parts, cancellous bone. Cortical bone comprises more than three‐fourths of the bone mass, and it has a relatively low porosity, ranging from 5 to 10% [31]. Porosity is a measure of the available fluid volume of bone. The soft inner spaces of bone, usually described as cancellous or trabecular bone, form the remaining one‐fourth of the bone mass [16, 31]. Cancellous bone has a high porosity from 60 to 90% and contains the bone marrow, which consists of blood stem cells, adipose cells, osteoblasts, and osteocytes [31]. Cortical bone is almost invisible and dark in conventional MRI. The reasons for this appearance are that bone consists of a relatively low number of hydrogen atoms, which are largely embedded in the bone mineral and, therefore, has a very short T2 relaxation time [32]. Cortical bone contains around 5–10% water by volume, most of which resides in the microscopic pores of the Haversian and the lacunocanalicular systems, a small fraction is bound to

8.2 ­Assessment of Bone Regeneration by Tissue Engineering with MR

collagen and matrix, and the rest is embedded in the crystals of the apatite mineral [33, 34]. This small fraction of the bone water protons has an extremely short transverse relaxation time (average T2 from 250 to 500 µs), while the echo times (TE) of the standard imaging pulse sequences are typically in milliseconds, therefore the bone appears dark in conventional MRI [10, 32, 35]. Recent MRI modalities development realized direct visualization of bone especially cortical bone [36]. Radial readouts in conjunction with short‐duration radiofrequency (RF) pulses enabled the signal acquisition to start tens of microseconds after excitation [37, 38]. However, these ultrafast MRI sequences are not widely available yet since they require special high‐speed imaging hardware [39]. Therefore, these developments are currently mainly applied to small animal devices. Animal MRI systems were initially constructed using lower field strength magnets, such as 2 T [40]. However, developments in the design of superconducting magnets have led to higher magnetic field strength instruments, such as 4.7, 7, 9.4, and 11.7 T and with corresponding higher strength imaging gradients provide spatial resolution in the range of 0.1–1.0 mm [41, 42]. Since the water binding to the mineral content of bone has no contribution to the contrast in conventional MRI, the predominant features of bone in MRI are located in the bone marrow [27, 36]. Contrast in the bone marrow reflects the microarchitecture and its fat/water composition. However, special care has to be taken when viewing the interface between the marrow and the surrounding solid bone tissue, where the transition between the marrow and cortical bone could induce magnetic field artifacts and distort the magnetic resonance (MR) images [16]. In tissue engineering, MRI is used for not only in vitro study of interaction between biomaterials and tissue/cells, but also in vivo monitoring of tissue growth, loss, and regeneration [21, 25]. In monitoring the bone growth in restoration and regeneration, MRI could provide greater detail in images of the beginning stages of bone growth based on its superior soft tissue contrast. In addition, MRI is also used as supplemental evidence to tissue development with other imaging modalities such as micro‐CT. In Washburn’s work, MRI (9.4 T) was compared with micro‐CT in an in vitro study for bone formation in a porous polyethylmethacrylate scaffold seeded with primary chick osteoblasts [25]. Growth was assessed over an 8‐week period by following the loss of MR signal in proton density MR images and the appearance of solid bone contrast in the micro‐CT images. In another study by Xu et al., a similar progressive decrease in the MR signal was observed in a 4‐week study of osteogenesis by MSCs embedded in a gelatin scaffold [39]. This study applied an 11.7‐T MR imager to monitor the bone formation and found a linear relationship between the increase in the concentration of bone minerals, such as calcium, and the decrease in the T1 and T2 relaxation times. The T2 was reduced by over 50% as the bone developed as shown in Figure 8.1, in which T2 had decreased similar

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(a) Long bone

Cancellous bone Articular cartilage

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Time (week) Figure 8.1  (a) An illustration of the anatomical structure of a long and cancellous bone, and a T1‐weighted axial MR image (field strength = 11.7 T; spin‐echo sequence TR/ TE = 500 ms/7 ms; slice thickness = 1 mm) through a cylindrical osteochondral plug excised from the distal femoral condyle. (b) T1/T2 bar chart and high‐resolution axial MR images for tissue‐engineered bone over a 4‐week period. The relaxation time data show that T2 is a good biomarker for studying the development of the construct, while T1 is less sensitive. The high‐resolution images were acquired with acquisition parameters: TR = 1 s; TE = 30 ms; slice thickness = 0.5 mm; field of view (FOV) = 0.8 cm × 0.8 cm; in‐plane resolution = 62.5 µm × 62.5 µm; and number of averages N = 8. The MR images show the consolidation of the construct (diameter decreases by 50%) and a fall in overall signal intensity with the incubation time. Relaxation times are represented by average ± STD (sample size n = 7). Xu et al. [39]. Reproduced with the permission of IOP Publishing.

to the results obtained by Washburn [25]. Both studies observed that the decrease in relaxation times correlated with bone mineral deposition—as measured by histological analysis on calcium composition and alkaline phosphatase activity. Furthermore, both studies reported that the apparent decrease in diffusion coefficient indicated the increase in structural content resulting

8.2 ­Assessment of Bone Regeneration by Tissue Engineering with MR

from the diffusion between interfaces and barriers after the introduction of the engineered bone substitute. On the other hand, in another bone growth study using 9.4‐T MR microscopy by Chesnick et al., ironically, increased T2 values were observed [43]. This study monitored the mineralization in a hollow fiber bioreactor seeded with embryonic chick osteoblasts for 9 weeks. In their study, proton density MR images, T2 relaxation time, and magnetization transfer ratio maps were obtained with a 78‐µm resolution for 1‐mm slices. The magnetization transfer ratio was found to change with mineralization in bone constructs. Higher but uneven values were observed in the mineralized zone, and lower values in the superficial region. These studies show the complexity of monitoring bone formation in different systems. Therefore, in the MR microimaging of bone formation, quantitative parameter measurements and histological controls are necessary [16]. Since tissue engineering materials and techniques are developing rapidly, there is an increasing need for in vivo monitoring of implant of grafts or scaffolds, first in animals and later in human clinical trials [38]. In an animal study by Hartmen et al., MRI was used to assess the growth and development of bone in rat model [38]. This study used high‐resolution MRI at 7.1 T to monitor the ectopic bone formation in a rat model over 7‐week period. The postimplantation MRI, X‐ray images, and histological results confirmed that MRI could detect small changes in bone growth as small as 0.5 mm in diameter, and could display the complete 3D shape of the newly formed bone. In another bone growth study using an athymic mouse model, Potter et al. used MR microscopy to evaluate the growth and development of tissue‐engineered phalange constructs [44]. MR microscopy images at 38th week time point showed that the engineered specimen to be about 3 mm longer and more heavily mineralized than controls. Meanwhile, an inverse linear relationship was found between the mineral concentration (measured by X‐ray micro‐CT) and the tissue hydration (measured by MR microscopy at 9.4 T). Such animal studies exhibited that MRI enabled the monitoring of the progressive changes of engineered bone grafts or scaffolds over a period of months, and MRI served as a complement to other noninvasive imaging technologies, for example, micro‐CT [45]. An important characteristic of bone healing and regeneration is mechanical strength [16]. MR elastography can assess the tensile strength through measurements of bone stiffness and elasticity. MR elastography was used to characterize the early stages of bone formation in gelatin scaffolds embedded with MSCs by Othman et al. [46, 47]. The shear stiffness of developing bone over a 2‐week period was measured, and a three‐ to fourfold increase was reported during the initial stages of growth compared with controls: 11.88±0.4 kPa at week 1 and 15.8±0.5 kPa at week 2 for the treated groups (compared with 4.1±0.3 kPa at week 1, and 3.6 ± 0.4 kPa at week 2 for controls). Considerable variations of the MR parameters from region to region were obtained due to an uneven growth of bone components within the sample. The results indicated

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the uneven distribution of stem cells inside the scaffolds; the reasons for this could be either uneven seeding of stem cells or heterogeneous migrations of stems cells due to uneven distribution of nutrients and growth factors. New methods are being developed to accurately detect and quantify the biochemical composition of tissues [11, 48–50]. This is particularly promising for osteochondral tissue engineering, where glycosaminoglycan (GAG), PG, and collagen content/orientation could be used as novel imaging biomarkers in addition to morphologic changes to evaluate cartilage and subchondral bone tissue regeneration and repair [51]. Techniques such as T1rho mapping, GAG‐ specific chemical exchange saturation transfer (gagCEST), and sodium MRI enabled contrast‐free quantification of GAG and PG contents through the correlation of T1rho relaxation times with bound water, or sodium signal intensity, to charged macromolecules in the cartilage ECM [52] (see Chapter 3 for more details on sodium MRI and Chapter 9 on MRI of cartilage). Similarly, delayed gadolinium‐enhanced MRI of cartilage (dGEMRIC) measured T1 relaxation times provides an indirect measurement and improved resolution of GAG content by utilizing the negative association between an administered negatively charged contrast agent and negatively charged GAG [48]. As a complementary approach, T2 mapping could be used to reproducibly measure structural and content changes in the collagen matrix, which has been validated in a number of clinical studies [48]. Although these techniques has great potential, beyond the general concerns regarding reproducibility, standardization of imaging parameters, and, for dGEMRIC, the risks of contrast use, the correlation of these biomarkers to tissue ingrowth in biomaterial scaffolds has yet to be explored in great detail. In this manner, the imaging goal would be to detect not only an increase or improvement in biomechanical and physiologic function, but also to identify the interactions between scaffold degradation and tissue repair. Additional consideration should focus on how the material would improve contrast with the tissue and if this was going to cause interference during imaging [42, 51, 53]. MRI has been employed extensively to visualize biomaterials and engineered tissues. One example is that the MR microscopy was employed to generate high‐resolution (5–10 µm) images of engineered tissues and biomaterials [38, 44]. Changes in collagen and mineral content in an osteoblast‐seeded bioreactor could be detected using different contrast schemes [19]. In proton density images, the lowest signal indicated highest mineralization and in MR images, the high signal indicated areas of high collagen content [43, 54]. MRI could track cells at both in vitro on engineered constructs and in vivo with contrast agents [55]. ECM deposition and mineralization could be visualized and quantified as well as biomaterial architecture in vivo [56]. Though the contrast agents used in these studies were nontoxic, safety identification is vital to any future clinical use of these contrast agents. In order to evaluate the in vivo response of implanted biomaterials, it is necessary to standardize methods thus to make quantifiable diagnostic conclusions about the biomaterial–host

8.3 ­MRI for 3D Modeling and 3D Print Manufacturing in Tissue Engineerin

interactions and degree of tissue repair [57]. It is also necessary to set up the healthy baselines for the in vivo animal study, since cartilage thickness and bone structure vary among species [58, 59]. One challenge to MRI is that it was difficult to evaluate both bone and cartilage simultaneously in a nondestructive manner due to image contrast and interference of soft and hard tissues, even though MRI could provide high‐ resolution and 3D models of bone and cartilage tissues [19, 50]. Furthermore, visualization and distinction of biomaterials from tissue is still a hard problem, and diagnostics of bone and cartilage repair often require validation from histological methods [29]. Several improvements have been made in recent years. For instance, 3D‐spoiled gradient recalled echo imaging with fat suppression (3D‐SPGR) and ultrashort echo time (uTE) were employed to evaluate the osteochondral junction by using high field strengths and optimized pulse time‐ echo sequences to measure the T1 and T2* (transverse relaxation detected in gradient‐echo imaging) relaxation times, respectively [48, 52, 60]. Improvements have also been made in image contrast by taking advantage of fat saturation, water excitation, and changes in molecular charge present in the diseased tissue [52, 61]. Although much has been done with uTE to detect the short T2/T2* relaxation times of the deep radial and calcified zones of cartilage, more investigations are needed for evaluating tissue engineering strategies in animal models [49, 62]. This is essential for translating preclinical results to human studies, since much higher magnetic fields are needed to improve the signal‐ to‐noise ratio (SNR) and resolutions in small animals, but could be achieved with lower fields for similar diagnostic application in humans. However, animal studies should adhere to ethical safety and care guidelines with respect to possible field thresholds in order to minimize scan time and thus tissue heating during imaging [63]. Fortunately, researchers have made efforts to standardize the sample preparation, calibration, scan parameters, and data analysis for consistent use of MRI [57, 64]. In general, the MRI technique is a forwarding technique to acquire optimized images of cortical bone, cancellous bone, cartilage, and relative ECM in macrolevel, with collagen and other components in micro‐ or cellular level.

8.3 ­MRI for 3D Modeling and 3D Print Manufacturing in Tissue Engineering MRI is the primary imaging modality used in 3D modeling of tissue along with CT and optical microscopy [24, 65]. Each technique has its own advantages and limitations as briefly described as follows. For bone tissue, particularly cortical bone, CT, or micro‐CT scans require exposure of a sample to small quantities of ionizing radiation, the absorption signal is detected and imaged, and then a series of 2D images displaying a density map of the sample are obtained [66–69]. Stacking of these 2D images creates a 3D

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representation of the scanned area. The main advantage of CT and micro‐CT as an imaging modality for 3D modeling in tissue engineering is its high resolution (on the order of microns). The development of micro‐CT technology could be applied to quantify the microstructure–function relationship of tissues and the designed tissue structures [70–72]. It included the characterized microarchitecture of scaffolds, the design and later fabrication of tailored tissue microstructures, the quantification of the bone tissue morphologies with internal stress–strain behavior, and the nondestructive evaluation of the porous biomaterials [6, 73, 74]. Optical microscopy has limited applications to 3D modeling due to the intensive sample and data manipulation. For example, in order to examine a sample with high resolution using optical microscopy, it has to be physically sectioned to a thickness of 5–80 µm and placed onto slides, providing a square sample at 1 cm × 1 cm for fine resolution. The slide division work is a labor‐ intensive process. The images of slides for the target organ would be thousands of 2D images or more. They have to be stacked into 3D columns and arranged in correct X and Y positions. This is computationally and a memory‐intensive process that challenged many computer modeling programs. From a practical point of view, pathologists are not expected to examine thousands of individual slides of an entire organ and identify every cell in the huge image stack. It is going to be a significant challenge to drive computer programs to identify individual cells by their visual characteristics, even with the aid of complex staining. However, optical microscopy is still a practical and probably the only tool to differentiate the tissue down to the individual cell level currently [1]. MRI provides images of not only the hard tissues but also the soft tissues, and it is superior in differentiating soft tissue types and recognizing border regions of tissues of similar density. For instance, Dhenain et al. performed micro‐MRI scans on mouse embryos and the resolution achieved was 20–80 µm/voxel [75]. The resulting segmentation isolated each of the major developing organs in the embryo. By using simple region growing techniques and Mimics software, they developed a 3D representation of the central ­nervous system, heart, and kidneys of the subject. CT is inferior to both MRI and optical microscopy in differentiating soft tissues with similar density, since differentiation of tissue in CT scans is accomplished through contrast segmentation, and the grayscale value of each voxel is determined solely by tissue density [76]. Hence, CT is much more effective in the modeling of hard tissues such as bone and sharply defined density changes, such as the interface between bone and soft tissues. In some cases, the disadvantage of poor soft tissue differentiation could be addressed with the introduction of contrast agents. The disadvantage of CT for soft tissue differentia­tion reflects the advantage of MRI in the same field. Despite the high tissue differentiation capacity, the resolution of MRI is consistently worse than both CT and optical microscopy. However, MRI is adept at assembling anatomic atlases with increasingly fine resolution as the technology becomes mature, and it provides more clinical options since the patient is not exposed to ionizing radiation [77, 78]. From a practical point of view, a hybrid modality approach

8.3 ­MRI for 3D Modeling and 3D Print Manufacturing in Tissue Engineerin

through two or more modalities would be appropriate for deciding a precise 3D model on the same specimen in order to address for deficiencies from any single modality. A 3D model derived from MRI and CT together could display both heterogeneous soft tissue, for which MRI is excellent, and a high‐resolution bone structure such as the skull, for which CT is better suited [79]. Designing and modeling of substitute scaffold or graft has always been the most important step in tissue engineering. The first step of the modeling is the reconstruction for 3D image representation. The entire process of the reconstruction 3D model from MRI/CT data is described in Figure 8.2 [1, 24]. In this scheme, the MRI/CT images are integrated using 2D segmentation and 3D region growth and this volumetric image data extracted more meaningful, derivative images via 3D anatomic view. The 3D anatomic view provides novel views of patient anatomy,

MRI/CT images

Voxel-based regeneration

2D segmentations

3d region growing

3d anatomy view

Anatomic modeling

Prototyping modeling

Volumetric representation

Contour-based generation

Model slicing

Volume rendering

3D shade surface extraction

Model processing

3D imaging representation

Medical modeling

Model-assisted application

Figure 8.2  Roadmap from MRI/CT to 3D Reconstruction. Redrawn from original scheme (with permission). Dark gray represents 3D Reconstruction, and light gray represents modeling and application [1, 24]. Reproduced with the permission of Elsevier.

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which could be used for volume rendering, volumetric representation, and 3D image representation. These 3D images lead to the generation of anatomic modeling, which could be used for contour‐based generation and 3D‐shaded surface representation of the medical models. Model slicing and signal processing lead to model‐assisted applications like those used in surgical planning, preoperative planning, and intraoperative planning. The 3D anatomical image and representation are usually constructed through either segmentation or volumetric representation. The 2D segmentation is the extraction of the geometry of the MRI/CT data set. Each slice is processed independently leading to the detection of the inner and outer contours of the living tissue. The contours are stacked in a 3D model and used as a reference to create a scaffold or graft model [40]. In general, scaffolds for bone usually have intricate architecture, porosity, pore size and shape, and interconnectivity in order to provide the needed structural integrity, strength, transport, and ideal microenvironment for cell and tissue ingrowth. The designed scaffolds could solely be fabricated through additive process, the so‐called 3D printing, to manufacture complex structural architectures. One example to create the 3D model of human femur through image registration, 2D segmentation, and 3D reconstruction by Mimics program (Materialise Co., Plymouth, MI, USA) is shown in Figure 8.3 [1]. Computer‐aided tissue engineering (CATE) promotes modeling, design, and fabrication of tissue scaffolds [65, 80]. CATE could not only apply to biomimetic design approach to introduce multiple biological and biophysical requirements into the scaffold design, but also could integrate both biomimetic and (a)

(c)

(b)

Figure 8.3  Femur example of image registration, 2D segmentation, and 3D reconstruction process. (a) CT images are loaded into and properly registered. (b) ROI is identified as appropriate differentiating color mask. (c) 3D voxel‐based femur model. Sun et al. [1]. Reproduced with the permission of Elsevier. (See insert for color representation of this figure.)

8.4 ­Assessment of Menisci Repair and Regeneration by Tissue Engineering with MR

Figure 8.4  Overall procedures of modeling and design of biomimetic bone scaffold. Sun et al. [1]. Reproduced with the permission of Elsevier.

nonbiomimetic features into the scaffold modeling database to form high fidelity and smart scaffolds [70]. Biomimetic features are either made upon real anatomical data from MRI/CT images, or created purely just within a program, such as channels and porous structures. Nonbiomimetic features that do not imitate nature could be designed as drug storage chambers, mechanical elements, and attachment interfaces for tubes, sensors, electronics, and other devices. Figure  8.4 describes the entire scene of CATE procedures both for representation of heterogeneous biological tissue structure, and for introduction of various design components of internal and external architecture, porosity, interconnectivity, mechanical properties, vascularization, and drug/growth factor delivery into the scaffold design [1].

8.4 ­Assessment of Menisci Repair and Regeneration by Tissue Engineering with MRI The meniscus in the knee joint is a crescent‐shaped connective tissue between the distal femoral and proximal tibial condyles that provides structural congruence and absorbs mechanical forces [81, 82]. The meniscus structure is composed of both a medial and a lateral component situated between the corresponding femoral condyle and tibial plateau as shown in Figure 8.5 [82]. Each

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Posterior Cruciate Ligament (PCL)

Femur Anterior Cruciate Ligament (ACL) Lateral Collateral Ligament (LCL)

Medial Collateral Ligament (MCL) Medial meniscus

Lateral meniscus

Transverse ligament

Tibia

Fibula

Figure 8.5  Anatomy of the knee joint: anterior view. The knee meniscus is situated between the femur and the tibia. Crossing the meniscus are various ligaments, which aid in stabilizing the knee joint. Kohn and Moreno [82]. Reproduced with the permission of Elsevier. (See insert for color representation of this figure.)

meniscus is a glossy‐white, complex tissue composed of cells, specialized ECM molecules, and region‐specific innervation and vascularization. Both menisci are critical components of a healthy knee joint [83, 84]. Human medial and lateral menisci have distinctly different dimensions: lateral menisci are approximately 32.4–35.7 mm in length and 26.6–29.3 mm wide, while medial menisci are 40.5–45.5 mm long and 27 mm wide [85, 86]. The meniscus could be distinguished into two distinct regions: the outer, vascular/neural region (red–red zone) and the inner, completely avascular/aneural region (white–white zone) [87]. These two areas are separated by the red–white region, which present attributes from both the red–red and white–white regions [88]. Though both menisci are roughly wedge‐shaped and semilunar, lateral menisci display greater variety in size, shape, thickness, and mobility than medial menisci [89]. Lateral menisci also cover a larger portion of the tibial plateau (75–93% laterally) in comparison to medial menisci (51–74% medially). Regarding composition by wet weight, the meniscus is highly hydrated (72% as water), with the remaining 28% composed of organic matter, mostly ECM and cells [90]. In general, collagens make up the majority (75%) of this organic matter, followed

8.4 ­Assessment of Menisci Repair and Regeneration by Tissue Engineering with MR

by GAGs (17%), DNA (2%), adhesion glycoproteins (