Atlas of Swept Source OCT and OCT Angiography 9811943907, 9789811943904

Table of contents :
Preface
Contents
About the Editors
1: An Introduction to Swept Source OCT
Introduction to Swept-Source Optical Coherence Tomography
Characteristics of Swept-Source OCT
Faster Speed
Higher Sensitivity and Signal to Noise Ratio (SNR)
Deeper Penetration
Deeper Imaging Range
Wider Field of View
Swept-Source OCT Angiography
OCT Angiography Basic Algorithm
Accurate Segmentation Algorithm
Projection Artifact Removal Algorithm (PAR)
Angiography Quantitative Metrics
Identify and Measure the FAZ Zone
Measurement of Retinal Blood Vessel Density and Related Vessel Metrics
Future Development of Swept-Source OCT
Faster
Deeper
Wider
Lower in Cost
More Intelligent
References
2: SS-OCT Image Acquisition and Analysis
Pre-inspection Preparation
Confirmation of Patient Information
Preparation for Image Acquisition
Swept-Source OCT Image
Eye Selection
Scan Mode Selection
Pupil Center Alignment
OCT Alignment
Eye Tracking Mode Selection
OCT Image Acquisition
Preview of Scan Results
Data Analysis
Line Scan Analysis
En Face Image
Thickness Analysis
Blood Flow Analysis
Manual Slab
Scientific Research Function
3: Swept-Source OCT of Normal Eyes
SS-OCT B-Scan of Normal Eyes
En Face SS-OCT of Normal Eyes
A-Scan for Commercial SS-OCT
En Face OCT
Quantitative Analysis of SS-OCT Volumetric Information
SS-OCT Quantitative Analysis of Optic Disc Correlation
SS-OCTA Images of Normal Eyes
Retina
Choroid
References
4: Vitreous-Related Disease
Posterior Vitreous Detachment
Posterior Vitreous Lamellar Fibers (Horizontal)
Centripetal Vitreous Fibers (Vertical)
Vitreous Hemorrhage
Posterior Vitreous Cortex Thickening
Posterior Cortical Vitreous Traction
Posterior Precortical Vitreous Pocket (PPVP)
Vitreous Opacity with Posterior Vitreous Detachment
Vitreomacular Traction Syndrome
Epiretinal Membrane
Clinical Grading System
SS-OCT and SS-OCTA
Macular Hole
Clinical Staging
SS-OCT and SS-OCTA
References
5: Pathologic Myopia
Retinal Lesions
Myopic Tractional Macular Degeneration (MTM)
Macular Hole
Retinal Break
Subretinal Hemorrhage
Outer Retinopathy
Epiretinal Membrane
Peripapillary Retinal Cavitation
Peripapillary Retinoschisis
Retinal Pigment Epithelial Tear
Choroidal and Bruch’s Membrane Lesions
Choroidal Neovascularization
Intrachoroidal Cavitation
Choroidal Atrophy
Uneven Diameter of Choroidal Vessels
Bruch’s Membrane Depression, Fracture
Sclera-Related Lesions
Scleral Depression
Scleral Splitting
Scleral Penetration of Blood Vessels
Optic Nerve-Related Lesions
Optic Disc Pit
Tilted Disc
Deepening of the Optic Cup
Other Lesions
Dome-Shaped Macula
Posterior Staphyloma
Subarachnoid Cavity
References
6: Age-Related Macular Degeneration
Non-neovascular AMD
Neovascular AMD
Serous Pigment Epithelial Detachment (With or Without Neovascularization)
Type 1 CNV
Polypoidal Choroidal Vasculopathy (PCV)
Type 2 CNV (Breaks Through the RPE Layer)
Type 3 CNV (Retinal Angiomatous Proliferation)
References
7: Central Serous Chorioretinopathy
8: Diabetic Retinopathy
Microaneurysm
Exudation
Edema and Fluid
Destruction of Capillary
Intraretinal Microvascular Abnormality
Neovascularization and Hemorrhage
Proliferative Membrane and Traction
Retinal Atrophy
Alterations of OCT and OCTA After Treatment of Laser Photocoagulation and Anti-VEGF Agents
9: Retinal Vein Occlusion
Central Retinal Vein Occlusion
Hemi-Retinal Vein Occlusion
Branch Retinal Vein Occlusion
10: Coats’ Disease & Mac Tel 2
Coat’s Disease
Macular Telangiectasia Type 2 (Mac Tel 2)
References
11: Retinal and Choroidal Inflammation
Infectious Uveitis
Retinal Vasculitis
Vogt-Koyanagi-Harada Disease
Behçet’s Disease
Punctate Inner Choroidopathy (PIC)
Acute Zonal Occult Outer Retinopathy (AZOOR)
Multiple Evanescent White Dot Syndrome (MEWDS)
Overlapping “White Dot” Syndromes
References
12: Genetic and Developmental Fundus Diseases
Retinitis Pigmentosa
Stargardt Disease
Congenital Retinoschisis
Chorioretinal Coloboma
References
13: Intraocular Tumor
Choroidal Hemangioma
Choroidal Osteoma
Retinal Capillary Hemangioma
Combined Hamartoma of the Retina and Retinal Pigment Epithelium
Choroidal Melanoma
Primary Intraocular Lymphoma
References
14: Miscellaneous
Retinal Arteriolar Macroaneurysm
Congenital Prepapillary Vascular Loop
Macular Cystoid Degeneration
Choroidal Neovascularization Secondary to Traumatic Choroidal Rupture
Idiopathic Choroidal Neovascularization
Ocular Ischemic Syndrome Secondary to Hypereosinophilic Syndrome
Hypertensive Retinopathy
Retinoschisis
References

Citation preview

Youxin Chen Xianzhao Peng Editors

Atlas of Swept Source OCT and OCT Angiography

·北 京·

123

Atlas of Swept Source OCT and OCT Angiography

Youxin Chen  •  Xianzhao Peng Editors

Atlas of Swept Source OCT and OCT Angiography

Editors Youxin Chen Department of Ophthalmology Peking Union Medical College Hospital Beijing, China

Xianzhao Peng SVision Imaging, Ltd. Luoyang, China

The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). A subsequent human revision was done primarily in terms of content. ISBN 978-981-19-4390-4    ISBN 978-981-19-4391-1 (eBook) https://doi.org/10.1007/978-981-19-4391-1 © Scientific and Technical Documentation Press 2023 Jointly published with Scientific and Technical Documentation Press The print edition is not for sale in China (Mainland). Customers from China (Mainland) please order the print book from: Scientific and Technical Documentation Press. This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publishers, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publishers nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publishers remain neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

It has been 20 years since the optical coherence tomography (OCT) imaging technology emerged as a revolutionary imaging modality that fundamentally improved the diagnosis in ophthalmology. Thanks to the continuous innovations, OCT technology has evolved from time-domain OCT, to spectral-domain OCT, and to now swept-source OCT (SS-OCT). The third generation of OCT technology, SS-OCT, is characterized by its fast scanning speed, high sensitivity, great penetration, deep imaging depth range, and wide field of view. Currently, SS-OCT technology is still in its early stage but is evolving rapidly. Contrasty OCT structural images of high resolution have become an indispensable diagnostic tool for ophthalmic diseases. In recent years, another breakthrough of OCT is OCT Angiography (OCTA), which obtains vascular images of the retina and choroid in vivo without the use of fluorescein dye or indocyanine green dye. OCTA can better reveal vascular morphology of the retina and choroid and detect early choroidal neovascularization by displaying the vascular structures on different anatomic layers. This technique is being widely used in the examination of many fundus vascular diseases. This atlas introduces the technical principles of SS-OCT imaging, as well as OCTA stratification algorithm and quantification. Most of the OCT and OCTA images in this book were taken with SVision’s SS-OCT, which showcases the capabilities of the latest OCT technologies. With a maximum imaging depth range of 6–12 mm, it is very useful in imaging pathological myopia, posterior scleral staphyloma, vitreous-related diseases, and intraocular tumors. In addition, the instrument features ultrawide angle optics. OCTA images of up to 87° (129° inner angle) can be obtained in a single scan. The automatic montage is able to provide an OCTA image covering the entire posterior hemisphere of 200° (inner angles). It serves as a noninvasive imaging modality to evaluate the severity of retinal vascular diseases such as diabetic retinopathy and retinal vein occlusion and emerges as an invaluable alternative to invasive fluorescein angiography. To help readers understand the disease from a multimodal imaging perspective, the cases in this atlas include not only SS-OCT and SS-OCTA, but also other imaging modalities including fundus fluorescein angiography (FFA), indocyanine green angiography (ICGA), and color fundus photography. All cases in this atlas are from the authors’ clinics. This atlas is aimed at ophthalmologists, technologists, residents in ophthalmology, and researchers in hope to advance the knowledge of SS-OCT and OCTA. This atlas would not have been possible without the efforts of a team of amazing residents and fellows from the Ophthalmology Department of Peking Union Medical College Hospital. We are grateful for their contribution in support of providing the cases. We would also like to express our sincere gratitude to Hongxia Chang, Yajing Ma, and Zhengming Shi for their assistance in image acquisition and image processing. Beijing, China Luoyang, China 

Youxin Chen Xianzhao Peng

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Contents

1 An  Introduction to Swept Source OCT�������������������������������������������������������������������    1 Xianzhao Peng, Zhengyu Wang, Jiayin Wang, and Youxin Chen 2 SS-OCT Image Acquisition and Analysis���������������������������������������������������������������   21 Ying Zhou, Chenjie Sun, and Youxin Chen 3 Swept-Source  OCT of Normal Eyes �����������������������������������������������������������������������   33 Jingyuan Yang, Erqian Wang, and Youxin Chen 4 Vitreous-Related Disease �����������������������������������������������������������������������������������������   55 Chenxi Zhang, Mingzhen Yuan, and Youxin Chen 5 Pathologic Myopia�����������������������������������������������������������������������������������������������������   67 Mingzhen Yuan and Youxin Chen 6 Age-Related Macular Degeneration�����������������������������������������������������������������������   97 Xinyu Zhao, Mingyue Luo, and Youxin Chen 7 Central Serous Chorioretinopathy �������������������������������������������������������������������������  115 Erqian Wang and Youxin Chen 8 Diabetic Retinopathy �����������������������������������������������������������������������������������������������  127 Jingyuan Yang and Youxin Chen 9 Retinal Vein Occlusion���������������������������������������������������������������������������������������������  155 Lulu Chen and Youxin Chen 10 Coats’  Disease & Mac Tel 2�������������������������������������������������������������������������������������  165 Bilei Zhang and Youxin Chen 11 Retinal  and Choroidal Inflammation���������������������������������������������������������������������  175 Huan Chen, Yuelin Wang, Bilei Zhang, and Youxin Chen 12 Genetic  and Developmental Fundus Diseases �������������������������������������������������������  205 Bing Li and Youxin Chen 13 Intraocular Tumor ���������������������������������������������������������������������������������������������������  215 Ruoan Han and Youxin Chen 14 Miscellaneous�������������������������������������������������������������������������������������������������������������  227 Huan Chen and Youxin Chen

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About the Editors

Youxin Chen  is the Director of the Department of Ophthalmology at Peking Union Medical College Hospital and the Director of Key Laboratory of Fundus Diseases at Chinese Academy of Medical Sciences. Prof. Chen has pioneered in the research on choroidal angiography in China. Also, included in his expertise are photodynamic therapy and anti-VEGF therapy. In addition, Prof. Chen has been actively engaged in clinical trials. He served as the principal investigator in international multicenter clinical trials “Brilliance Study” and “VIVID EAST Study.” All the studies achieved remarkable outcomes. To date, Prof. Chen has published more than 100 articles in academic journals, including JAMA Ophthalmology, Retina, and IOVS in the last 5 years. He also serves on the editorial board of many ophthalmology journals, such as BMC Ophthalmology, Chinese Journal of Ophthalmology, and Chinese Journal of Ocular Fundus Diseases. In addition to his position as Director of the Department of Ophthalmology at Peking Union Medical College Hospital, Prof. Chen also serves as the Chair of Beijing Ophthalmologist Association, Vice President of Chinese Ophthalmologist Association, and Deputy Director of Ophthalmologic Committee of Chinese Non-government Medical Institutions Association. Prof. Chen was presented Distinguished Service Award by Asia-Pacific Academy of Ophthalmology (APAO) in 2008 for his efforts to promote the development of ophthalmology in China. In addition, Prof. Chen received the “Outstanding Doctor Award” in 2015, “Achievement Award” in 2016 and 2018 from APAO, and “Outstanding Leadership Award” in 2018 by the Overseas Chinese Association for Vision and Eye Research. Xianzhao Peng  is the founder of SVision Research. Inc (California), SVision Imaging, Ltd (China), and the Chief System Architect of the van Gogh series of SS-OCT devices for ophthalmic imaging. Dr. Peng graduated from Oregon State University with a PhD Degree in chemical physics in 2002 and has taken a number of engineering and executive roles in the industry of lasers and precision optics and ophthalmic imaging. Dr. Peng started his industrial career at New Focus in 2001, where he developed his expertise in external cavity wavelength tunable lasers (the predecessor of modern swept source). He joined KLA-Tencor Corp. in 2007 and served as a key optical expert to develop state-of-the-art semiconductor inspection tools based on ultraprecision optics. In 2014, along with a team of industrial veterans, he founded SVision Research with a mission to bring advanced ophthalmic imaging technologies to ophthalmic clinical practices worldwide. His team successfully developed and commercialized world’s first SS-OCT device running at 200,000 A-scans per second and featuring unprecedented imaging depth range and wide field of view. Since then, SVision continues to push to the technical frontiers and arises as a new leader in ophthalmic imaging. Dr. Peng has 15 academic publications and more than twenty technical patents in the areas of laser spectroscopy, precision optics, and ophthalmic imaging. He now serves as the Chief Technology Officer of SVision and is leading the company’s effort of technical innovations.

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An Introduction to Swept Source OCT Xianzhao Peng, Zhengyu Wang, Jiayin Wang, and Youxin Chen

I ntroduction to Swept-Source Optical Coherence Tomography

could be constructed from the OCT raw data. If the scanner does a 2D raster scan, 3D images of the sample could be reconstructed. Optical coherence tomography (OCT) is a noninvasive nonIn terms of principle and application, OCT shares some contact imaging modality [1]. Based on partial coherence similarities with ultrasound imaging that has been widely interferometry, it detects the reflected and scattered light used in medical imaging. Both emit a probing beam and capfrom samples at different depth. With scanning, 2D or 3D ture the information along the propagating direction by anaimages could be reconstructed from the reconstructed signal lyzing the returned signal, both reconstruct 2D or 3D images and the scanning timing. by scanning the probing head, as shown in Fig. 1.2. The difIn the field of medical imaging, OCT has become one of ference is that ultrasound imaging detects sound signal, the most successful optical imaging modalities ever since known as the “echo,” and gets the z position of the samples microscopes were invented. OCT imaging does not need any by analyzing the timing of the echo, whereas in OCT, the florescence agent. It is characterized with low radiation, fast probing beam and returned signal are both light, and OCT operation, and high resolution. Especially for ophthalmic determines the z position of the samples by analyzing the imaging, OCT is able to reveal the fine detail of the layered interference signal, known as the interference fringes. structure of the retina, which makes it an indispensable The axial resolution of OCT is determined by the spectral imaging tool for diagnosis of eye diseases such as aged-­ width of the light source. In ophthalmic applications, the related macular degeneration (AMD), diabetic retinopathy axial resolution is typically on the order of a few microns, (DR), pathological myopia, and glaucoma. which is at least two orders of magnitude better than the axial OCT technology is based on low coherence interferome- resolution of ultrasound imaging computed based on echo try. As shown in Fig. 1.1, a light source emits a probing beam timing. Besides, because the wavelength of light wave is of broad spectral bandwidth, which passes a beam splitter, much shorter than that of sound wave, the lateral resolution part of the light goes to the reference arm. The other part of OCT is similar to that of a microscope of similar numerigoes into the sample arm and reaches the sample. The light is cal aperture, and at least one order of magnitude better than reflected and/or scattered and comes back along the original ultrasound. On the other hand, because OCT relies on propaillumination path. Then the returned light is recombined with gation of light wave, it suffers higher loss in biological tisthe reference light and interference occurs. By analyzing the sues than ultrasound and therefore has limited penetration interference fringes, the information of the sample along the depth. Therefore, while ultrasound has been widely used for light propagates (z direction) within a certain depth could be imaging large organs in internal medicine, OCT fits better in obtained. If a scanner (a “galvanometer” or a MEMS mirror) ophthalmology where the media in human eyes are transparis employed to scan the probing beam, a 2D tomography ent or semi-transparent to near infrared light. Other applications of OCT include dermatology, intra-vascular imaging in cardiology, and endoscopy in gastroenterology. X. Peng · Z. Wang · J. Wang Since its invention, OCT has evolved through three genSVision Imaging, Ltd., Luoyang, Henan, China eration [2]. e-mail: [email protected] The first generation of OCT technology is known as Y. Chen (*) Time Domain OCT.  The axial information of the layered Department of Ophthalmology, Peking Union Medical College Hospital, Beijing, China biological structure is obtained by scanning the reference e-mail: [email protected]

© Scientific and Technical Documentation Press 2023 Y. Chen, X. Peng (eds.), Atlas of Swept Source OCT and OCT Angiography, https://doi.org/10.1007/978-981-19-4391-1_1

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Fig. 1.1  Principle of OCT

Reference Mirror

Beam Splitter

Sample

Light Source

Photo Detector

a

b

Fig. 1.2  Ultrasound Image vs. OCT image (a): ultrasound image of an eye; (b) OCT image of a healthy eye corresponding to the portion inside the yellow box in the ultrasound image. OCT is able to reveal much finer detail than ultrasound

mirror along the optical axis and therefore changing the optical path length of the reference arm. Whenever the optical path length of the reference arm matches the optical path of the interface within the sample, there is a peak signal on the detector. The first generation of OCT established its unique value in ophthalmology. Compared to the traditional ultrasound imaging modalities its axial resolution is improved by an order of magnitude. While ultrasound imaging requires placing the transducer on the cornea of a patient, the OCT imaging process is completely noninvasive and does not require any direct contact with the patients’ eyes. Because of the aforementioned advantages, OCT quickly replaced ultrasound imaging in retina imaging ever since the beginning of the millennium. But the

TD-OCT suffers a number of limitations such as low sensitivity and low imaging speed. Thus, it is not suitable for 2D or 3D imaging, and the imaging process is susceptible to eye movement. The second generation of OCT devices were introduced to clinical use around 2005. A spectrometer that comprises a grating and a Charge-Coupled Device (CCD) array replaced the photo detector in TD-OCT to detect the spectrum of interference between the reference light and the back reflection or scattering from the sample. The information along the probing light propagation can be deduced by applying a Fourier transform on the interference spectrum detected by the spectrometer. This scheme is called Fourier Domain or Spectral Domain (SD)-OCT.

1  An Introduction to Swept Source OCT

In SD-OCT, the reference reflector stays stationary. The information along the optical axis of the probing beam within the imaging depth range is acquired simultaneously, thus the scan speed in terms of number of A-Scans that can be acquired is improved by two orders of magnitude. The sensitivity is also improved. The improvement of speed and sensitivity makes 2D OCT imaging much more practical and also makes 3D imaging possible. Since first commercialized in 2006, SD-OCT received widespread acceptance by ophthalmologists and became the primary imaging modality in ophthalmic clinical practice. The main limitation of SD-OCT is the limited imaging depth, which can only reach 1.8–2.2 mm in tissue. SD-OCT is not able to provide satisfactory imaging in applications which requires high imaging depth such as high myopia, pathological myopia, and posterior scleral staphyloma. The limited imaging depth also make it unsuitable for ultra-­ widefield retina imaging and anterior segment imaging. The third generation of OCT is known as Swept Source OCT (SS-OCT). It was first invented in mid-1990s. A swept source, in which the wavelength of the emitted laser can be tuned continuously, is employed instead of a broad band light source as in TD-OCT and SD-OCT.

1996-2002

TD-OCT 100 AScans/s

2002

TD-OCT 400 AScans/s

2006 to now

SD-OCT 26,000 to 70,000 AScans/s

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In 2006, Professor Fujimoto’s team of MIT invented a swept source called Fourier Domain Lock Mode (FDLM) laser of ultrahigh tuning speed [3]. The system built with FDLM greatly increased the speed of OCT.  Around 2012, swept source based on vertical cavity surface emitting laser (VCSEL) became commercially available [4–8]. The SS-OCT systems built upon VCSELs not only surpass SD-OCTs in terms of both speed and sensitivity but also overcome the shortcoming of limited imaging depth of SD-OCT [9]. Imaging depth longer than 100 mm was demonstrated [9]. SS-OCT, especially the systems based on 1060 nm semiconductor lasers, represents the future of ophthalmic OCT [10]. Figure 1.3 illustrates the evolution of OCT technology. In 1996, the early commercial OCT came out. Although the image quality was still relatively rough, for the first time, it allowed in vivo observation of the retina structure that was never seen before, therefore it attracted great academic and clinical interest. In 2002, the speed and image quality of the second-generation products have improved a lot compared with earlier products, but the overall performance still fell short of the level of practical clinical use. In 2006, the frequency domain OCT came out, which significantly increased

2019 SS-OCT 100,000 to 200,000 AScans/s

Fig. 1.3  Evolution of OCT technology from Time Domain, Spectral Domain to Swept-Source OCT

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the scan speed to 27,000/s. Meanwhile, the image quality was also improved significantly, and thus resulted in the widespread popularity of OCT imaging. OCT has become an indispensable tool for ophthalmic examination and subsequently became the golden standard for the diagnosis of retina diseases. Since 2012, the swept source OCT has improved ophthalmology OCT images to a new height. With higher speed, better image quality, deeper depth, and wider range, SS-OCT opens a new era to ophthalmic OCT imaging.

Characteristics of Swept-Source OCT SS-OCT has a number of advantages over SD-OCT:

Faster Speed The typical speed of SD-OCT ranges from a few thousand A-Scans per second of the early products to a few several tens of thousands of A-Scans per second of the latest SD-OCT products. The speed of SD-OCT is determined by the speed of the linescan camera of the spectrometer. The latest SD-OCT systems for research can go up to 250,000 A-Scans per second. However, inevitably the increase of the speed is achieved at the cost of the significant degradation of image quality. Ultrafast SD-OCT remains limited in clinical applications. The speed of SS-OCT is determined by the speed of the swept source, the speed of data acquisition and the data

X. Peng et al.

processing power of the computer. At present, swept sources with sweeping speed of 100,000 and 200,000 sweeps per second have been commercialized. From 2012 to 2020, 100,000 A-Scans/s is the primary speed on the commercially available SS-OCTs. In 2019, SVision Imaging (Luoyang, China) launched the first 200,000 A-Scans/s ophthalmic SS-OCT (SVision VG200). The device obtained CFDA clearance in 2019 and started marketing in China. Five months later, Carl Zeiss revealed 200,000 sweeps/s ophthalmic SS-OCT (Elite 9000 Release 2) in EU and US as well. As a point-scan imaging technology, the increase of scan speed allows denser scanning and higher pixels in en face planes. This is critical for en face OCT and OCT angiography. Figure  1.4 is a high resolution widefield SS-OCT Angiography of a healthy eye taken by a VG200 SS-OCT of SVision Imaging running at 200,000 A-Scans per second. It takes 2,100,000 A-Scans in total to complete such an OCTA image. The central area keeps fine detail of the capillaries, which is nearly impossible with SD-OCT. At present, the high-end SS-OCT of 200,000 A-Scans per second mainly serves the frontier of clinical research and large ophthalmology-specialty clinics. It will gradually become the workhorse of ophthalmic imaging tool in the next few years while SS-OCT systems running at 400,000 A-Scans per second and one million A-Scans per second will also be commercialized in the next few years, bringing more possibilities for high resolution OCTA, real-time 3D OCT imaging, real-time OCTA, and intraoperative OCT microscopes.

Fig. 1.4  High resolution widefield SS-OCT Angiography, 12 mm × 12 mm 1024 × 1024 pixels

1  An Introduction to Swept Source OCT

 igher Sensitivity and Signal to Noise Ratio H (SNR) The detection module in SD-OCT is a grating-based spectrometer. Due to the complex structure, grating-based spectrometers usually suffer from considerably high loss. In contrast, SS-OCT uses a balanced photodetector with relatively simple structure for light detection and thus the light loss can be minimized. Besides, the balanced detector can effectively suppress common mode noise. Therefore, at the same speed, the SS-OCT exhibits higher sensitivity and image signal-to-noise ratio (SNR) than the SD-OCT. Figure 1.5 is an OCT image of a healthy retina. SS-OCT reveals the layered structure with a remarkable SNR.  The fine detail of the vitreous cortex and the liquefaction cavity are shown in SS-OCT, which are usually hidden in the shadow of SD-OCT images. Besides, SS-OCT penetrates well through the choroidal layer and reaches the sclera, making it an ideal imaging modality to study the lesions in the choroidal layer. In clinical practice, the refractive medium of the eye could be opaque due to cloudy lens or vitreous hemorrhage, which may result in a weak OCT signal. In such situations, the penetration capability and high sensitivity of the SS-OCT become particularly crucial. Figure  1.6 is a comparison of the images acquired from a cataract patient in SD-OCT and SS-OCT, respectively. The retina structure is barely visible in the SD-OCT scan (left) and the image quality is inadequate for diagnosis. In comparison, the same retina is well-­ imaged in the SS-OCT thanks to its high sensitivity. The structural characteristics revealed provides an important basis for clinical diagnosis.

Deeper Penetration Most SD-OCT systems work in near IR band, typically around 850 nm, whereas SS-OCT systems work in 1050 nm band or 1300 nm band. Longer wavelength leads to less scattering and better penetration. Therefore SS-OCT at 1050 nm has a great advantage in revealing the detail of the choroidal layers and sclera. As shown in Fig. 1.7 below, SS-OCT (left) clearly reveals the fine structure in the choroidal layer and

Fig. 1.5  Widefield OCT image of the retina of a healthy adult

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the choroidal-sclera boundary. In comparison, SD-OCT (left) is not able to penetrate to the choroidal layer and the choroidal-sclera boundary completely unidentifiable.

Deeper Imaging Range Both SD-OCT and SS-OCT reconstruct images from the detected spectrum of the optical interference between the reference light and the light returned from sample arm. The imaging depth depends directly on the spectral resolution. The spectral resolution of SD-OCT is limited by the grating-­ based spectrometer. The sensitivity drops sharply with the increase of the imaging depth. In contrast, the spectral resolution of SS-OCT depends on the intrinsic linewidth or the coherence length of the laser source, the sweeping speed, and the data acquisition rate. The coherence length of a swept source of single longitude mode could well exceed 100 mm. With a modern high speed data acquisition card, the achievable optical resolution could be much higher than a traditional grating-based spectrometer, therefore achieve an image depth several times that of SD-OCT, as shown in Fig. 1.8. At present, the typical imaging depth of SD-OCT is between 1.8 and 2.2  mm, whereas a typical SS-OCT is 2.6–3  mm, and the most advanced commercially available SS-OCT could reach 12 mm in tissue at a sweeping speed of 100,000 A-Scans per second, or 6 mm in tissue at 200,000 A-Scans per second.

Wider Field of View Another benefit from the increase of imaging depth is the expansion of the achievable field of view. The anatomical structure of human eyes contains an inherent curvature in OCT images. i.e., the retina is not flat in the OCT image even if the eye is perfectly healthy. From the point view of optics, the optical path from the pupil to the curved retina varies with field points even on emmetropic eyes. The further away from the center, the shorter the optical path. The deviation increases rapidly when it goes toward the edge of the field of view. Therefore, the field dependency of optical path results in a curved shape of the retina in OCT images. If the edge portion falls out of the available depth range, it will stay in the OCT image and appears to be a blurred mirror image of the real sample, as shown in Fig.  1.9a. This is known as “foldover artifact” and is prominent in widefield imaging in which the optical path difference on the edge differs considerably from the center. High myopia will make foldover artifact even worse because the shape of the eyeball is prolonged, deviates from a sphere, and thus increases the curvature significantly. Therefore, when the field of view expands, the OCT system needs larger depth range to cover the edge portion and prevent foldover artifact.

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Fig. 1.6  SS-OCT retinal image (left) vs. SD-OCT image of the same eye, acquired from a patient with cataract

Fig. 1.7  Comparison of penetration capability between SD-OCT (left) vs. SS-OCT (right)

Depth Comparison of SS-OCT and SD-OCT

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SS-OCT SD-OCT

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Fig. 1.8  Signal strength of OCT with respect to depth. The sensitivity of SD-OCT rolls off quickly with depth, whereas SS-OCT has much larger depth, and the signal strength remains flat in a large portion of the total depth

On SD-OCT systems, a wideangle field of view up to 60° can be achieved with proper lens design; however, due to the limitation of imaging depth, foldover artifact is a prominent issue and OCT imaging of large field of view is not practical in clinical use. SS-OCT overcomes the depth limitation by significantly improving the detection resolution of the interference spectrum. When the image depth reaches 2.7 mm in-tissue, most patients can perform widefield imaging. When it reaches 6 mm in-tissue, most clinical patients, including those with severe pathological myopia and posterior scleral staphyloma, can be imaged without visible foldover artifact, as shown in Figs. 1.9b and 1.10.

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Fig. 1.9  OCT images of different penetration depths in pathologic myopia. (a) Note the foldover artifact (arrows) in the B-Scan of a standard image depth of 3 mm. (b) No foldover artifact is eliminated in the B-Scan of 6 mm Super Depth™

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Fig. 1.10  Widefield SS-OCT images in high myopia. (a) SS-OCT 16 mm scan of high myopia; (b) SS-OCT 16 mm scan of posterior scleral staphyloma

Swept-Source OCT Angiography One of the most important progress made in recent years in OCT is OCT Angiography (OCTA) [11, 12]. As red blood cells flow through blood vessels, the OCT signals reflected from these blood cells also change over time. For stationary

tissues, there is no such change. Thus, by imaging the same location at different times and computing the difference of OCT signals, imaging of the blood vessels is obtained with stationary tissues suppressed up to the limit of shot noise (Fig. 1.11). OCTA provides similar information as fluorescein angiography without the needs for an injectable dye, making it a

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Fig. 1.11  OCTA images extracted from repeated structural B-Scans at the same location

Fig. 1.12  Comparison of montage color fundus photograph and SS-OCTA ultrawide field montage image of a patient with bilateral central retinal vein occlusion (CRVO)

noninvasive, faster, and safer investigation. In addition, the morphology of vascular network can be shown layer by layer. OCTA has been reported to be a crucial imaging technique in the diagnosis and understanding of many retinal conditions, such as retinal vascular occlusion and neovascu-

larization (Figs. 1.12, 1.13, and 1.14). In recent years, with the widespread application of anti-VEGF agents, OCTA is becoming a standard functionality of OCT product. High quality OCTA imaging requires high quality hardware and advanced algorithm.

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OCT Angiography Basic Algorithm

Fig. 1.13  Widefield montage OCTA imaging of severe nonproliferative diabetic retinopathy. SVision Imaging’s montage ultra-widefield OCTA image covers a view of 80° × 60° and clearly shows capillary dropout, microaneurysm, and intraretinal microvascular abnormality (IRMA)

Fig. 1.14  Widefield montage OCTA imaging of proliferative diabetic retinopathy(PDR). SVision Imaging’s ultra-widefield OCTA clearly reveals a variety of lesions (arrows): (1) non-perfusion area, (2) neovascularization, and (3) pre-retinal hemorrhage

From the hardware aspect, the high SNR of a scanning-­ spectrum laser enables higher sensitivity of OCTA images. The high scanning speed of SS-OCT allows higher resolution and lesser motion artifact. The high penetration depth of SS-OCT translates to a much stronger imaging capability of deep choroidal vessels. Ultrawide field of view also allows physicians to examine the peripheral region of retina within a single scan that was not possible in previous devices. From the software perspective, a few key algorithms are required:

Due to the limitation of data acquisition, some early generations of angiography algorithm typically compute an OCTA image from a single OCT image, by analyzing the phase and amplitude differences between neighbor A-Scans. Such approach is only sensitive to fast blood flow, thus only sensitive to medium to large vessels, and is practically not able to discern small and capillary vessels with low blood speed. In addition, because there is inherent structural difference between neighbor A-Scans, the OCTA image usually is a combination of blood vessel and structural difference. With the vast improvement of data acquisition speed, the standard method nowadays is to acquire multiple B-Scans at the same slow scan position. OCTA signals are computed through analyzing the phase or amplitude differences between these B-Scans. This method not only provides excellence resolving power for capillary blood vessels, it also allows multiple frame averaging when there are more than two B-Scans, and significantly improves the SNR of OCTA image as a result. Each OCT product may employ a different OCTA formula. Despite the variety, most can typically be categorized as one of the following methods: phase based method, amplitude based method, or a combination of the two [13, 14]. The difference results in a different dependency of the result’s SNR on different system noise sources. Whether to use phase can be tricky. On the one hand, difference in phase is no doubt a very important channel of information. In an ideal setup, it can provide an indispensable boost of resolving power. On the other hand, phase-based method can be much more sensitive to eye motion than amplitude base method, and an algorithm that utilizes phase needs to have an additional so-­called motion compensation algorithm to suppress motion artifact. For SS-OCT system, phase jitter between different A-Scans can have a strong impact on phase-based algorithm unless it is properly taken care of. Overall, a well-designed algorithm should be properly optimized against different noise sources of a particular hardware system to bring the best out of such a system.

Accurate Segmentation Algorithm Segmentation algorithm is an indispensable step in OCTA algorithm. Different anatomical layers of human retina have vastly different vascular networks. It often takes a comprehensive examination of different layers to reach a proper diagnosis. For example, the main diagnosis of choroidal neovascularization is from the OCTA image of the avascular layer right above the RPE and Choroid interface (Rpe-Ch). Thus majority of today’s OCTA product are presenting

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results using projections from different anatomical layers. Without a fast and accurate segmentation algorithm to identify the boundary of these layers, it is often impossible to make effective and accurate diagnosis. For a long time, segmentation algorithm employs a so-­ called graph cut algorithm. The basic concept of such an algorithm is to mathematically construct a cost function based on the difference of OCT intensity on the two sides of a layer interface, then find the shortest path through the cost function. This method is very effective for continuous and relatively flat layer structures in healthy retina. However, for pathological cases, it can happen that certain layers become warped, thickened, or thinned, and sometimes even completely ruptured. For these cases, graph cut algorithm often fails to produce an accurate result. An example is illustrated in Fig. 1.15. SVision Imaging has developed a deep learning-based segmentation algorithm called Deep Layer algorithm (DL) to specifically overcome such challenges. A deep convolutional neural network is constructed and customized targeting the specific characteristics of OCT images and retinal anatomical layers. Enormous amount of data collected

from a large patient base, including many different types of pathological cases, are labeled by ophthalmological experts and fed to the neural network for training. Through many iterations of training and feeding new data, the neural network can effectively pick up the optical characteristics of different retinal layers and their relative relationship, as well as ­possible changes to these characteristics and structures for pathological cases. When the data of a new patient is sent to the network for prediction, it can produce very accurate segmentation results. See the example illustrated in Fig. 1.16. The deep learning-based Deep Layer algorithm can not only give accurate prediction on stratification in pathological data, but also has the advantage of scalability. When a new case occurs and the existing algorithm fails to give accurate segmentation result, one simply needs to feed the new cases to the AI model for retraining, rather than redesigning the algorithm. In terms of computational speed, the algorithm based on deep learning is no less than traditional algorithms with the support of the superior power of today’s graphic processors.

Fig. 1.15  Precise segmentation is crucial for OCTA. The upper row is the projection of blood flow signals of different layers, from left to right: superficial capillary layer, deep capillary layer, avascular layer, and choroid capillary layer. The blood flow signals of avascular layer

and corresponding en face OCT images are shown in the left and middle of the lower row. The corresponding B-scan OCT image and that superposed with blood flow signal image are presented in the lower right

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Fig. 1.16  Comparison between traditional segmentation algorithm and deep learning algorithm. (a) Conventional algorithm fails to a pathologic retina properly. (b) Deep learning-based algorithm precisely segments the layers of the same pathologic retina

Projection Artifact Removal Algorithm (PAR) Based on the principle of OCT, whenever a scanning beam of laser has reached a deeper layer B, it must have already penetrated an upper layer A at the same scanning position which is positioned closer to the light source than B. Thus a perturbation of light caused by A becomes part of the measurement when we measure the light reflected from B.  If there is a blood vessel in A that forms an OCTA signal at A, we will see a similar signal from the OCTA signal at B.  Clearly, when there is no blood vessel in B at the same scanning location, such a signal is not originated from a real blood vessel

at B, which is in fact the shadow of a blood vessel in A, thus is called projection artifact. The presence of projection artifact is a serious nuisance and can directly impair a physician’s judgment on OCTA images of the layers affected. If we don’t remove these projection artifact from these layers, whenever a physician examines this layer, he or she must simultaneously refer to all layers above to ensure a proper reading. That will lead to tremendous extra work and is prone to misdiagnosis. Figure 1.17a is a practical clinical example. When the artifact removal function turned off, the artifacts projected by the upper vessels were prominent. A good OCTA product

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Fig. 1.17  Comparison of projection artifacts removal algorithm off and on at the avascular layer. (a) With PAR off, the projection artifact from the upper vessels is prominent. (b) With PAR on, the projection artifact is well suppressed

must be able to automatically remove projection artifacts to clear the hurdle for physicians. Due to the complexity of the artifact generation mechanism, the most common artifact removal algorithms nowadays are empirical formulations. They either reduced the angiography signal of deeper layers based on some experimentally chosen formula, or simply use all-or-nothing logic to either keep or to remove the signal. To evaluate the effectiveness of the projection artifact removal algorithm, one needs to check not only if all projection artifacts have been removed as much as possible from a given layer but also whether the intrinsic vascular network that belongs to that layer has been well preserved. It is apparently undesired to inadvertently remove true vascular information as a result of PAR. This is especially important for detection of neovascularization. If a neovascularization was removed because of PAR, it defeats the purpose of projection artifact removal because both key information and nuisance

are removed, rendering the reading useless without the key information. A successful example can be seen in Fig. 1.17b. When PAR is turned on, only the projection artifacts are removed, the neovascularization is well preserved.

Angiography Quantitative Metrics The need to produce quantitative diagnosis using angiography images has been quickly pushing this technology towards quantitative measures. An assortment of quantitative metrics derived from OCTA images are becoming well recognized and being utilized by many ophthalmologists as valuable tools for diagnosis. Some of the well-known metrics include the physical dimension and geometrical description of foveal avascular zone (FAZ), blood vessel density near fovea, and percentage of retinal flow void zone. Angio metrics allow physicians to quantitatively describe the cur-

1  An Introduction to Swept Source OCT

rent status of lesion, and compare different stages of its development, thus enabling rigorous comparative diagnosis and treatment. Next we will elaborate on two of the commonly used metrics.

I dentify and Measure the FAZ Zone The macula is located at the end of the capillary covering of the retina. Inside the fovea of the manula of healthy people’s eyes exists a regular-shaped zone, where there is no blood vessels in the superficial and the deep inner retina layers. This zone is called FAZ. The boundary of FAZ is defined as a smooth inner envelope of the last ring of capillaries before entering the FAZ, such as the red line shown in Fig. 1.18. FAZ measurement usually uses the angiography image of the inner retina layer, because this layer has the best representation of the capillary morphology in macula. An algorithm needs to automatically identify and locate the boundary of the FAZ, and avoid distractions from any other avascular region that may exist inside retina. Once the FAZ boundary has been correctly identified, its geometric size and shape can be described by standard formulas. Those commonly used are area, perimeter, and circularity index. Circularity index is defined as: Circularity index =

4π × area perimeter 2

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Fig. 1.18  Metrics related to FAZ: (a) 3 mm × 3 mm inner retina angiography en face image of a norm eye (FAZ area 0.188 mm2, peripheral 1.719 mm, circularity index 0.80, FD-300 49.61); (b) 3 mm × 3 mm

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The circularity index of normal human eyes can usually reach 0.8–0.9. The lower the circularity index, the more irregular the shape of FAZ. Another related metric is factual dimension (FD), defined as the vessel density of a region which spans between the FAZ boundary and its extension outwards by a fixed distance. The most commonly used distance is 300 μm, yielding a metric named FD-300. For certain types of retinal disease, the physical dimension and shape of FAZ may show significant changes. Diabetic retinopathy, for example (Fig. 1.18b), shows an enlarged FAZ, with reduced circularity index and reduced FD. Precise identification and measurement of the size and shape of FAZ are important quantitative indicators used to describe and diagnose such diseases.

 easurement of Retinal Blood Vessel Density M and Related Vessel Metrics The small blood vessels and capillaries in the fundus of normal people are relatively evenly distributed, except for some special areas like the macular area and the optical nerve head. Diseases such as blood vessel obstruction, neovascularization, diabetic retinopathy, and glaucoma can directly or indirectly change the distribution of blood vessels. The measurement of blood vessel density is an important tool for quantitatively identifying and diagnosing such diseases. Metrics are usually defined as a percentage of the area of blood vessels in a local region to the total area of the region.

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inner retina angiography en face image of an eye with diabetic retinopathy (FAZ area 0.497 mm2, peripheral 3.237 mm, circularity index 0.60, FD-300 36.45)

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The core algorithm for measuring blood vessel density is to correctly distinguish the boundary between the foreground (vessel) and the background (avascular area). The main challenge is how to achieve consistent boundary segmentation for large vessel and capillaries at the same time, and to produce reliable result for data with different illuminations, signal to noise ratio, and sampling rates. Commonly used algorithm uses traditional threshold-based method for image segmentation. The selection of the threshold is critical and directly affects the repeatability and comparability of the results. In the near future, deep learning-based algorithm may be utilized to provide more rigorous algorithmic support to improve the reliability of the results. The definition of the percentage of non-perfusion area is very similar to that of the blood vessel density mathemati-

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cally. The difference is that the non-perfusion area is concerned with the percentage of the area where there is no blood flow. The algorithm also needs to accurately distinguish the background (avascular area) and the foreground (vessels). Many vascular diseases show noticeable changes in blood vessel density. As shown in Fig. 1.19, areas with higher blood vessel density are shown in red and those with lower blood vessel density in dark blue. The blood vessel density of a 3 mm × 3 mm region in the macular area of a normal eye is relatively uniform. On the other hand, that of a patient with diabetic retinopathy is significantly sparser. Patients with branch retinal vein occlusion show large areas of blood vessel loss at the site of blockage. Figure 1.20 shows the change of blood vessel density in a 6 × 6 mm area of the posterior pole.

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Fig. 1.19  Blood vessel density metrics in 3 mm × 3 mm superficial inner retina en face. (a) Normal eye; (b) diabetic retinopathy; (c) Branch retinal vein occlusion

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Fig. 1.20  Blood vessel density metrics in 6 mm × 6 mm superficial inner retina en face. (a) Normal eye; (b) diabetic retinopathy; (c) Branch retinal vein occlusion

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Future Development of Swept-Source OCT Compared to Ultrasound, Computer Tomography (CT), and Magnetic Resonance Imaging (MRI), OCT is still a young technology. It has just been 30  years since the technology was invented, less than 20 years since it is adopted as a powerful clinical imaging modality, but it has brought profound impact to ophthalmic diagnosis. Today, OCT technology is still dynamically and rapidly evolving. From the technical point of view, ophthalmic OCT is advancing and becoming faster, deeper, and wider. In addition, the maturity of key components will bring cost reduction, and the development of artificial intelligence will make OCT imaging equipment smarter in the future.

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imaging speed. In fact, this technology has been demonstrated in academia. However, due to the high cost, bulky volume, and the challenges in ultrahigh-speed data acquisition and processing, the SS-OCT of more than one million A-Scans/s has not been commercialized. With the gradual advances of the technology and the higher demand brought by the clinic applications, commercially available ultrahigh-­ speed OCT devices will enter clinical research in the next few years and provide a powerful means to realize dynamic 3D OCT imaging, ultra-widefield high-resolution OCTA imaging, real-time OCTA imaging, and 3D OCT intraoperative navigation and bring a new chapter of SS-OCT.

Deeper

The typical imaging depth of SD-OCT devices is usually 1.8–2.2 mm, whereas that of SS-OCT is 2.6–3.0 mm nowaThe smallest unit of an OCT image is an axial scan known as days. In most clinical cases, it is sufficient to image the strucan A-Scan. In SS-OCT, it corresponds to a single sweep of ture from the retinal nerve fiber layer (RNFL) to the choroid the laser. 2D or 3D OCT images are formed by scanning a layer. However, the depth is not sufficient for retinas with pair of mirrors (galvanometers) point by point. At each point, high myopia, pathologic myopia, posterior staphyloma, etc. an A-Scan is acquired. The most advanced SS-OCT devices due to the foldover artifact. The foldover artifact is especially such as SVision Imaging VG200 and Carl Zeiss Elite 9000 prominent in widefield imaging, and it necessitates a signifiRelease 2 are able to acquire 200,000 A-Scans per second. cant increase of imaging depth. As for ultra-widefield imagThe resolution of an angiography image can be as high as ing beyond 70° (approximately 20  mm on retina), an 1024 × 1024 pixels. While they are able to maintain the fine extended depth becomes a prerequisite. For example, detail of capillaries on a widefield scan of 42°  ×  42°, the Professor Ohno’s team used a SS-OCT prototype of 5 mm resolution is still insufficient for ultra-widefield imaging imaging depth to obtain 23 mm ultra-widefield OCT images above 72°. A higher resolution requires a higher A-Scan rate. of patients with high myopia, and systematically studied the The higher A-Scan rate can also reduce the artifact due to morphological changes of posterior vitreous in patients with the eye-motion of patients. Clinically, in many complex situ- high myopia [15]. The 5 mm in-tissue depth enabled a comations, especially when the patient’s fixation is poor, even the plete imaging of the eyes of high myopia without foldover fastest SD-OCT systems are still unable to complete high-­ artifact. resolution OCTA imaging within the time that the patient In Retina China 2019, SVision Imaging presented the can fix his/her vision on a target. In order to further improve development result of a new OCT prototype featuring an the clinical availability of the device and the field of view enhanced imaging depth of 4.5  mm in tissue, with a comand resolution of OCTA imaging, it is desirable to seek OCT mercial name of Super Depth™. The imaging depth was furdevices of higher A-Scan rate. Nowadays, commercially ther increased to 6 mm in tissue in subsequent development, available OCT devices of 400,000 A-Scans/s have emerged, and a few months later, at the annual conference of Asia-­ although the installation base is still extremely limited. The Pacific Vitreo-Retina Society Congress (APCRS) in Nov ultrahigh speed is particularly valuable in ultrawide angle 2019, SVision Imaging demonstrated the first ophthalmic OCTA where dense sampling is essential. However on the imaging device that is able to image both posterior and anteother hand, the high speed also creates a challenge in achiev- rior segments at an in-tissue depth of 6 mm and field of view ing sufficient imaging depth which is also critical in ultra- of 16 mm. Shortly after, the imaging depth of anterior segwide angle imaging. From the point of view of clinical ment is further expanded to 12 mm in-tissue (16 mm in air). applications, the best tradeoff of speed and imaging depth is Figure 1.21 is a set of retinal and anterior segment images still to be evaluated. Nevertheless, with the advancement of taken by the SS-OCT system. The significant increase of technical innovation, it is expected that the OCT devices of imaging depth is expected to help the study of pathologic 400,000 A-Scans/s will become the mainstream high-end myopia, posterior staphyloma, retinal tumors, choroidal ophthalmic OCT devices in the next few years. tumors, retina detachment, etc. (Fig. 1.22). SS-OCT products of one million A-Scans/s or above will The latest single longitudinal mode swept laser such as also emerge and enable applications demanding ultrahigh vertical cavity surface emitting laser (VCSEL) [7, 8] has a

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coherence length of well beyond 100  mm. With adequate data acquisition speed, OCT systems built with such lasers can readily achieve an imaging depth of 60 mm (45 mm in tissue), which is ideal for biometric applications. The ­following picture is taken from a biometric device prototype of SVision Imaging based on SS-OCT. It reveals the overall structure of a healthy eye from the cornea, the anterior chamber, the lens to the retina. The imaging depth of the system reaches 40 mm in-tissue. A single capture of image will complete the most critical axial measurements including axial eye length, anterior chamber depth, corneal thickness, lens thickness. The biometric device prototype is also capable of imaging the complete anterior segment. After ray-tracing-based optical correction, corneal curvature,

Wider Ultra-widefields as large as 72–87° have recently become available options on the high-end ophthalmic OCT devices to image the lesions on the peripheral retina (Figs. 1.24 and 1.25). After Mosaic, the field of view could reach 135°, corresponding an inner angle of approximately 200° and covering the full rear hemisphere of the eye (Fig. 1.26).

6mm

16mm

sclera angle, lens curvature, and other key biometric parameters can be derived. The high speed of the SS-OCT makes three-dimensional imaging practical. It can not only obtain corneal topography through image analysis but also can reconstruct the complete three-dimensional optical form of the entire anterior chamber from the cornea to the lens. The powerful 3D imaging of the anterior segment will help tremendously on ophthalmic diagnosis, cataract surgeries, and enable precise customization in refractive surgeries and IOL implantation (Fig. 1.23).

Lower in Cost

Fig. 1.21  OCT image of pathologic myopia. SVision Imaging’s ophthalmic OCT prototype with Super Depth™ achieves an in-tissue depth of 6 and 16 mm widefield scan length

SS-OCT is still very expensive today. The main cost comes from the light source itself, the core of SS-OCT.  But the good news is, the latest several types of swept sources are largely based on the well-scalable semiconductor process. Thousands of laser chips could be produced out of a single InGaAs wafer. With the advancement of technology and volume production, the cost of the swept source will drop in a similar trend as semiconductor chips of integrated circuits.

Fig. 1.22  SVision Imaging’s SS-OCT featuring dual imaging modes of both posterior and anterior segments, achieving an imaging depth of 6 and 12 mm in-tissue, respectively

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CT ACD

LT

VIT

AXL

Fig. 1.23  Full-eye imaging of super depth SS-OCT. SVision Imaging’s OCT prototype achieves an in-issue imaging depth up to 40 mm and covers a 16 mm field of view. CT cornea thickness, ACD anterior depth, LT lens thickness, AXL axial length

Fig. 1.25  87° × 72° (26 mm × 21 mm) OCTA image from a healthy retina. Image acquired by SVision ImagingVG200 SS-OCT with 87° ultrawide angle add-on lens Fig. 1.24  56° × 56° (72° diagonal) OCTA image from a healthy retina. Image acquired by SVision Imaging VG200 with 72° wideangle ocular lens

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Fig. 1.26  OCTA images of 200° (inner angle) FOV as montage of five ultrawide angle OCTA scans. (a) Retina Layer. (b) Choroidal Layer. The vortex veins (arrows) are clearly visible

In the next 3–5 years, the cost of SS-OCT will drop to similar level as SD-OCT and will quickly replace SD-OCT with its superior performance.

The aforementioned pioneering work has a profound meaning on ophthalmic imaging. However, fundus photography lacks information of the three-dimensional structure of the retina. The eye diseases recognizable solely based on fundus photography is limited. AI software based on fundus photogMore Intelligent raphy can only be used for screening of very specific diseases to specific patients, e.g. screening of diabetic r­etinopathy of In recent years, artificial intelligence (AI) attracts lots of diabetic patients. The real-world clinical practice or routine attentions and brings revolutionary development to medical health checkups require AI-aided diagnosis to not only detect imaging [16]. In ophthalmology, color fundus photography specific diseases but also diagnose all possible diseases. As a is the earliest application on which AI is focused [17–21]. In result, the current AI-aided diagnostic software is not able to 2016, a research team of Google trained a deep Convolutional meet the requirement of the actual need of clinical practice. In Nerve Network (CNN) with nearly 130,000 color fundus comparison, OCT images are able to provide a more complete photos and implemented automatic recognition of diabetic depiction of the 3D structure of the retina and have established retinopathy (DR) and diabetic edema (DE). The models as the diagnostic golden standard of retina diseases. In recent achieved a sensitivity and a specificity both above 90% on years, more and more AI research teams shift their interest the test library [17]. In 2017, a research team of Johns from fundus photography to OCT images [24, 25]. Hopkins University realized automatic recognition of Age-­ In 2018, Cell published a cover report of a research team related Macular Degeneration (AMD) based also on deep of the University of California San Diego (UCSD) applying CNN and color fundus photography. The accuracy is 88.4– AI algorithms on OCT images [25]. Using the transfer 91.6%, which is similar to the level of ophthalmologists [18]. learning algorithm, the UCSD team trained a CNN with In 2018, US Food and Drug Administration (FDA) approved 100,000 OCT B-Scans to identify choroidal neovascularthe first AI-based software IDx-DR for diabetic retinopathy ization (CNV), diabetic edema (DE), and drusen from OCT (DR) screening [21], marking the beginning of AI technol- images. The accuracy, sensitivity, and specificity are all ogy going out of laboratories for practical clinical use. above 95%. Recently, a research team of Guangzhou Zhongshan Compared to AI models based on color fundus photograOphthalmology Center applied AI on ultra-widefield retina phy, the biggest difference and challenge of OCT-based AI is imaging, and achieved automatic detection of lattice degen- that the amount of available data has been increased by one eration in the peripheral retina [22]. Another team from the to two orders of magnitude. The color fundus photography same institute explored the efficacy of an AI agent using from a single eye produces one image in most cases. But to deep learning in diagnoses and treatment of congenital cata- cover the same area of a fundus, a typical OCT scan proracts [23]. duces dozens to hundreds of images and contains the addi-

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tional dimension of information which fundus photograph does not. If only sparse OCT linescans are conducted on the retina, lesions may be missed. In 2018, Nature Medicine magazine reported Google Deepmind’s work [26]. The research team demonstrated a two-step training scheme and studied the deep learning algorithms over 3D volume data. The scheme employed two sets of deep learning networks. The first set of network conducts pixel-level segmentation on 3D OCT images of retina. The resulted segmentation models go into the second network and are further analyzed. The trained AI models under this scheme are able to recognize and categorize more than ten most-common retina diseases. The accuracy is in par with ophthalmologists. More importantly, this framework can readily be expanded. Theoretically all retina diseases known to human experts could be covered, and thus it opens the possibility to meet the need of clinical diagnosis of real world. With the continuous advancement of OCT hardware and AI technology, the following trends will emerge in future ophthalmic practice: OCT-based AI models will become the mainstream of ophthalmic applications and overtake the primary responsibility from screening to aided diagnosis of eye diseases. Compared to fundus photography, OCT provides 3D volume data and reveals a more comprehensive picture of the retina, allowing detection of lesions hidden below the surface (i.e., the RNFL layer) of the retina. In large hospitals and ophthalmology specialty clinics, rapid 3D scans will become the essential imaging protocol. The embedded AI models will automatically recognize 3D volume data and help ophthalmologists precisely locate the lesions for further close-up scanning. Artificial intelligence-guided image acquisition will also effectively reduce the chance of omissions in diagnosis or human errors. With the widespread adoption of ophthalmic AI, the capability of recognizing multiple diseases will become an essential requirement. AI models based on structural OCT can already recognize a variety of retina diseases. The arising OCTA technology further adds capability to noninvasively observe the microcirculation of the retina. For lesions in retina microcirculation such as CNV, ophthalmologists rely on combined information of the OCTA images and corresponding structural B-Scans to correctly diagnose diseases of microcirculation such as CNV. In the future, the ophthalmic AI will take two or more imaging modalities into account including OCT, OCTA, fundus photography, and traditional angiography to further improve the accuracy of disease detection and cover all eye diseases known to humans’ knowledge. AI software will be integrated into ophthalmologists’ diagnosis process. Most of the current ophthalmic AI products require a manual exportation of images after image acquisition from the imaging acquisition software. Some AI

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products require users to further upload the exported images to the cloud for AI software to analyze. AI interpretation comprises a few steps off the usual work flow of ophthalmologists’ diagnosis. In the next few years, it will become a trend that AI software products are integrated with imaging devices and PACS systems. In this way, AI interpretation will become an integrated part of the ophthalmologists’ existing diagnostic processes and thus improve the efficiency of clinical practice. AI software will be integrated with hardware as well and make the hardware more intelligent. One-click collection and even unmanned OCT devices are under development in academic and OCT manufacturers’ laboratories. On such prototypes, AI software becomes an integrated part of the system and serves as a hidden but powerful engine in the backstage working throughout the entire process from image acquisition, analysis, and optimization to interpretation. After image acquisition is completed, AI will provide detailed textual description and quantification of the lesions including the location, range, size, shape, and other characteristics. It will not only further free the doctors from routine works of imaging interpretation and reporting but also prevent potential omission of lesions of human doctors. Because the retina is the only organ where the structure of the microcirculation and nerve fibers can be observed directly, OCT-based AI will likely to help diagnose various systemic diseases, for example, cardiovascular diseases such as hypertension and hyperlipidemia, neurodegenerative diseases such as Alzheimer Diseases and multiple sclerosis [27]. It is foreseeable that the development of OCT imaging technology and the combination of AI and OCT will bring profound change to ophthalmic diagnosis and better serve human health.

References 1. Huang D, et  al. Optical coherence tomography. Science. 1991;254:1178–81. https://doi.org/10.1126/science.1957169. 2. Drexler W, Fujimoto J.  Optical coherence tomography technology and applications. 2nd ed. Springer; 2015. https://doi. org/10.1007/978-­3-­319-­06419-­2. 3. Huber R, et al. Fourier domain mode locking (FDML): a new laser operating regime and applications for optical coherence tomography. Opt Express. 2006;14(8):3225–37. https://doi.org/10.1364/ OE.14.003225. 4. Kuznetsov M, et  al. Compact ultrafast reflective Fabry-Perot tunable lasers for OCT imaging applications. Proc SPIE. 2010;7554:75541F-2. https://doi.org/10.1117/12.842567. 5. Derickson D, et al. SGDBR single-chip wavelength tunable lasers for swept source OCT.  In: Proceedings Volume 6847. Coherence domain optical methods and optical coherence tomography in biomedicine XII; 2008. 68472P. https://doi.org/10.1117/12.761039. 6. Minneman MP, et al. All-semiconductor high-speed akinetic swept-­ source for OCT. Proc SPIE. 2011;8311(831116):831116–0.

20 7. Potsaid B, et al. MEMS tunable VCSEL light source for ultrahigh speed 60 kHz-1  MHz axial scan rate and long range centimeter class OCT imaging. Proc SPIE. 2012;8213:82130M, 82130M-8. https://doi.org/10.1364/ACP.2011.831116. 8. Jayaraman V, et  al. OCT imaging up to 760  kHz axial scan rate using single-mode 1310nm MEMS-tunable VCSELs with >100nm tuning range. In: 2011 conference on lasers and electro-optics: laser science to photonic applications, CLEO; 2011. https://doi. org/10.1364/QELS.2011.PDPB2. 9. Grulkowski I, et  al. Retinal, anterior segment and full eye imaging using ultrahigh speed swept source OCT with vertical-cavity surface emitting lasers. Biomed Opt Express. 2012;3(11):2733–51. https://doi.org/10.1364/BOE.3.002733. 10. Drexler W, et al. Optical coherence tomography today: speed, contrast, and multimodality. J Biomed Opt. 2014;19(7):071412. https:// doi.org/10.1117/1.JBO.19.7.071412. 11. Gao SS, et al. Optical coherence tomography angiography. Invest Ophthalmol Vis Sci. 2016;57:OCT27–36. https://doi.org/10.1167/ iovs.15-­19043. 12. Baran U, Wang RK. Review of optical coherence tomography based angiography in neuroscience. Neurophotonics. 2016;3(1):010902. https://doi.org/10.1117/1.NPh.3.1.010902. 13. Zhang A, et  al. Methods and algorithms for optical coherence tomography-based angiography: a review and comparison. J Biomed Opt. 2015;20(10):100901. https://doi.org/10.1117/1. JBO.20.10.100901. 14. Mahmud M, et  al. Review of speckle and phase variance optical coherence tomography to visualize microvascular networks. J Biomed Opt. 2013;18(5):050901. https://doi.org/10.1117/1. JBO.18.5.050901. 15. Takahashi H, et  al. Ultra-widefield optical coherence tomographic imaging of posterior vitreous in eyes with high myopia. Am J Ophthalmol. 2019;206:102–12. https://doi.org/10.1016/j. ajo.2019.03.011. 16. Chen Y, et al. Insights and prospectives of ophthalmologic artificial intelligence technology 2019;35(2):119–23. https://doi. org/10.3760/cma.j.issn.1005-­1015.2019.02.003. 17. Gulshan V, et  al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402–10. https://doi. org/10.1001/jama.2016.17216.

X. Peng et al. 18. Burlina PM, et al. Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks. JAMA Ophthalmol. 2017;135(11):1170–6. https://doi. org/10.1001/jama.2016.17216. 19. Abràmoff MD, et al. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci. 2016;57(13):5200–6. https:// doi.org/10.1167/iovs.16-­19964. 20. Li Z, et al. An automated grading system for detection of vision-­ threatening referable diabetic retinopathy on the basis of color fundus photographs. Diabetes Care. 2018;41:2509–16. https://doi. org/10.2337/dc18-­0147. 21. The U.S.  Food and Drug Administration, FDA permits marketing of artificial intelligence-based device to detect certain diabetes-related eye problems. https://www.fda.gov/news-­events/ press-­a nnouncements/fda-­p ermits-­m arketing-­a rtificial-­ intelligence-­b ased-­d evice-­d etect-­c ertain-­d iabetes-­r elated-­eye, FDA News Release, 11 Apr 2018. Accessed 22 Jan 2020. 22. Li Z, et  al. A deep learning system for identifying lattice degeneration and retinal breaks using ultra-widefield fundus images. Ann Transl Med. 2019;7(22):618. https://doi.org/10.21037/ atm.2019.11.28. 23. Long E, et al. An artificial intelligence platform for the multihospital collaborative management of congenital cataracts. Nat Biomed Eng. 2017;1:0024. https://doi.org/10.1038/s41551-­016-­0024. 24. Prahs P, et al. OCT-based deep learning algorithm for the evaluation of treatment indication with anti-vascular endothelial growth factor medications. Graefes Arch Clin Exp Ophthalmol. 2018;256:91–8. https://doi.org/10.1007/s00417-­017-­3839-­y. 25. Kermany DS, et  al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell. 2018;172:1122–31. https://doi.org/10.1016/j.cell.2018.02.010. 26. De Fauw J, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med. 2018;24:1342–50. https:// doi.org/10.1038/s41591-­018-­0107-­6. 27. Marziani E, et al. Evaluation of retinal nerve fiber layer and ganglion cell layer thickness in Alzheimer’s disease using spectral-­ domain optical coherence tomography. Invest Ophthalmol Vis Sci. 2013;54:5953–8. https://doi.org/10.1167/iovs.13-­12046.

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SS-OCT Image Acquisition and Analysis Ying Zhou, Chenjie Sun, and Youxin Chen

This chapter introduces the routine acquisition and analysis procedure of OCT imaging, including line scan, 3D scan, angiography scan, as well as analysis of retinal and choroidal tomographic structure, thickness, en face image, and OCTA quantification, using the van Gogh SS-OCT as an example. This chapter focuses on the general procedures and methods of image acquisition and analysis, and readers should refer to the equipment operation manual for specific details. The following sections describe the preparation of examination, OCT image acquisition, and data analysis.

Pre-inspection Preparation After opening the van Gogh software and completing the system self-test, the software enters the patient database interface, as shown in Fig. 2.1. The upper left corner of the interface (area 1) is the patient information filtering area. The upper right side (area 2) is the patient information entry area. The left side of the interface (area 3) is the patient list. The right side (area 4) is the list of the current patient’s completed data collection, and the lower right side (area 5) shows the data preview images.

Confirmation of Patient Information After a patient is seated, the patient’s information (area 2) should be entered first, including name, gender, and date of birth. If there is a patient with the same name, the software will automatically prompt to verify if the patient with the same name is the same person by birthday, gender, etc., and confirm with the patient. If it is indeed a new patient, click “New” to create a new patient record. Then double click on the patient record entry in area 3 to enter the data collection interface.

Preparation for Image Acquisition Before examination, clean and disinfect the mandibular and frontal rests or use disposable pads. Adjust the electric lift table to a suitable height so that the patient can sit in a comfortable position to facilitate the subsequent examination. The patient is instructed to place the lower jaw in the center of the mandibular tray, place the forehead forward against the frontal tray and look at the fixation lamp. Once the patient’s information is confirmed, he/she is ready for image acquisition.

Y. Zhou · C. Sun SVision Imaging, Ltd., Luoyang, Henan, China Y. Chen (*) Department of Ophthalmology, Peking Union Medical College Hospital, Beijing, China e-mail: [email protected] © Scientific and Technical Documentation Press 2023 Y. Chen, X. Peng (eds.), Atlas of Swept Source OCT and OCT Angiography, https://doi.org/10.1007/978-981-19-4391-1_2

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Fig. 2.1  Patient database interface. (1) Patient information filtering area; (2) Patient information entry area; (3) Patient list; (4) Scan data list; (5) Data preview area

Swept-Source OCT Image The Van Gogh SS-OCT is equipped with three image modules: an OCT imaging module, a pupil camera, and a confocal scanning superluminescent ophthalmoscope (cSSO). The OCT imaging module is the primary imaging mode, the pupil camera is used for alignment of the patient’s pupil, setting the working distance, and monitoring the OCT scanning process, and the cSSO imaging is used to fine-tune the working distance and provide the precise localization of lesion and scan area. The cSSO image is also used for eye-tracking during the scan to ensure high quality results, which is especially important for 3D and OCTA scans. Figure 2.2 shows the image acquisition interface. After entering this interface, the three image modules are automatically launched. The top left side of the page (area 1) is the pupil camera window, the bottom left side (area 2) is the cSSO window, and the right side of the page (area 3) is the OCT preview window. The page provides several controls for mode selection and adjustment. Area 4 provides a choice of scanning modes, area 5 is for eye selection, area 6 is for pupil camera control, area 7 is for fixation light selection, area 8 is for OCT image adjustment, area 9 is for eye tracking mode selection, and area 10 is for scan start and stop controls.

Several buttons for automatic adjustment are also provided on the page. Auto Camera Alignment automatically aligns the pupil camera to a patient’s pupil and sets the working distance once the patient’s pupil is in the pupil camera field of view (area 11). The “Automatic OCT alignment” automatically adjusts to optimize the cSSO and OCT images (area 12). “Fully Automatic Alignment” adjusts the pupil camera, cSSO, and OCT in sequence, enabling intelligent one-button operation (area 13). The following procedure should be followed for OCT image adjustment and acquisition to obtain the best OCT images for subsequent analysis.

Eye Selection The left or right eye of a patient is selected according to the need for examination (area 5), and if both eyes are to be examined, it usually starts with the right eye.

Scan Mode Selection Different scanning modes are selected depending on the patient’s condition (area 4). For patients with poor refractive media, it is recommended to choose a scan mode with a

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Fig. 2.2  Image acquisition interface. 1–3 are view windows: (1) pupil camera; (2) confocal fundus image; (3) OCT live image; 4–8 are selection and adjustment areas: (4) scan mode selection, (5) eye selection, (6) pupil camera adjustment, (7) fixation lamp selection, (8) OCT

image adjustment; (9) tracking mode selection; (10) capture, termination Auto camera alignment; (11) pupil alignment; (12) Auto OCT alignment; (13) Fully automatic alignment

higher number of repetitions, such as Single-line HD.  The length, angle, and spacing of the scan lines can be adjusted according to the actual situation. For 3D and flow imaging scans, different scanning ranges are selected according to the patient’s actual condition (the recommended scanning range is comparable to the dimension of a lesion of interest).

enhancement (area 12); or adjust focus, OCT image position, and OCT signal enhancement individually with the button (area 8). Area 8). The real-time OCT signal intensity determines whether the OCT image has been adjusted to the optimal state. The signal strength ranges from 0 to 10, with 0–5 displayed in red, indicating a weak OCT image signal; 5–8 displayed in yellow, indicating a medium signal; and 8–10 displayed in green, indicating a strong signal.

Pupil Center Alignment Select the “Auto Camera Alignment” button in the Pupil Camera Adjustment Area to complete one-touch automatic alignment (area 11) or adjust the position of the eye with the up, down, left, right, front, and back buttons individually (area 6), so that the center of the pupil camera window coincides with the center of the patient’s pupil and the corneal reflection point is sharpest.

OCT Alignment Click the “Auto OCT Alignment” button in the OCT image adjustment area to complete one-touch OCT auto alignment, including focus, OCT image position (centering) and signal

Eye Tracking Mode Selection Different eye tracking modes (area 9) are selected according to the patient’s cooperation, namely high quality (high eye tracking accuracy), high speed (selected when the patient’s cooperation is not so good to improve tracking efficiency), and off (tracking off). The eye-tracking mode can also be switched off during the scanning process according to the actual situation. In some patients, the cSSO image signal intensity is very low due to the severe refractive media opacity, leading to failure of tracking and scanning. In this case, the tracking mode can be changed from high quality to high speed to complete the scanning. However, some of the patients still cannot be scanned even in high-speed mode. In

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Fig. 2.3  Scanned image preview screen

this case, the tracking mode can be turned off, and the patient is instructed to complete the scanning by staring at the target without blinking to complete the scan.

the image quality is poor due to severe eye movements or other reasons, click “Cancel” to discard the current data and reenter the scan screen to reacquire.

OCT Image Acquisition

Data Analysis

Once a B-scan image is optimally adjusted, the image is captured by clicking on the “Capture” button, or clicking the middle mouse button for quick collection. During the acquisition process, the center position of OCT images can be quickly fine-tuned by sliding the mouse wheel to ensure that the images will not be folded during wide-field scanning.

Line Scan Analysis

Preview of Scan Results After the scan is completed, the image and OCT signal intensity can be viewed in the image preview screen (Fig. 2.3) to evaluate image quality. Click “Save Capture” to save the current data and reenter the scan screen for subsequent data acquisition; or click “Save Analysis” to save the current data and directly enter the analysis screen to analyze the data. If

The Van Gogh SS-OCT offers a wide range of line scans, such as high-definition single line, multi-line, and star scans. The position, length, angle, and height of the line scan can be adjusted so that the clinician can choose according to the actual clinical needs. Take the 12 mm line scan for example (Fig. 2.4). By scanning multiple lines at the lesion site, the pattern of the lesion can be observed from different line cuts, and the currently selected B-scan is shown in the upper area 1 of Fig. 2.4. Lower area 2 of Fig. 2.4 shows the index of all scans at all locations. The mouse arrow, mouse wheel, or keyboard arrow keys can be used to quickly switch among B-scans at different locations. The lower left area 3 shows the scan position and range of the OCT in the cSSO image, as well as the currently selected scan line.

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Fig. 2.4  12 mm line scan analysis interface

En Face Image For all 3D scanning modes, including all OCTA scanning modes, van Gogh software can render en face images of each layer based on precise segmentation and present the extent of the lesion from another perspective (Fig.  2.5). Figure  2.5 Area 1 is the index of each layer, area 2 is the enlarged en face image, and area 3 is the cSSO image. The operator can drag the horizontal or vertical line in the cSSO image or en face image, and the lower area 4 shows the corresponding vertical and horizontal OCT B-scan image and the details of the layer segmentation.

Thickness Analysis For all 3D scan modes, including all OCTA scan modes, the van Gogh software gives a variety of thickness analysis, such as retinal thickness and choroidal thickness, based on precise segmentation. Figure 2.6 shows the page for thickness analy-

sis. Area 1 is for layer selection. An operator can select the layer of interest, and area 4 shows its two-dimensional thickness distribution in topographic map accordingly. Similarly, the operator can drag the horizontal or vertical line in the cSSO image (area 2) or thickness map, and area 3 on the right shows its corresponding OCT B-scan image and the details of the segmentation.

Blood Flow Analysis By collecting and analyzing the changes in OCT signals at the same location to distinguish blood flow from static tissue, OCTA technology can extract the morphology of the vascular network from 3D OCT data and display the morphology of the fundus vessels layer by layer, providing a new perspective to observe retinal and choroidal diseases. Combined with intelligent segmentation and SS-PAR artifact removal technology, it can accurately display the vascular morphology of each layer.

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Fig. 2.5  En face image and cSSO image

Figure 2.7 shows the blood flow imaging analysis interface. Area 1 contains the OCTA en face images of various layers. The definition of each layer is shown in Table 2.1. An operator can select a layer of OCTA images in area 1, and area 2 shows a magnified version of the selected OCTA image. Area 3 is the OCT en face image of the corresponding layer superimposed on the cSSO image, and the operator can switch focus between the en face and cSSO images by dragging the transparency bar below. The lower area 4 is a pair of otherwise identical B-scan images, with the blood flow signal superimposed in red on the left side, and the same B-scan without the blood flow signal on the right side. The operator needs to combine the flow signal and the layer information in the B-scan to make a comprehensive assessment of the image during the observation of the blood flow, which is particularly important for the detection of neo-

vascularization. The tools in the lower right area 6 can be used to adjust the upper and lower boundaries of a selected layer, or to adjust both boundaries simultaneously by “moving layers” to precisely observe the location and extent of the changes in the blood vessels and to facilitate better visualization of the lesion. In addition, for complex lesions, van Gogh software offers a more flexible manual adjustment of the layers, which will be described in the next section. The Van Gogh SS-OCT software provides different tools to quantify retinal blood flow. In Fig. 2.8, area 5, the operator can select the desired quantification function, such as FAZ analysis (Fig. 2.8a), blood flow density (Fig. 2.8b), and blood flow perfusion area (Fig.  2.8c). These quantification tools can be used to detect early changes in superficial retinal vascular lesions and to quantify the follow-up effect of treatment by comparing the data to achieve accurate treatment of fundus lesions.

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Fig. 2.6  Thickness in topographic map

Fig. 2.7  Blood flow imaging data analysis interface

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28 Table 2.1  Definition of retinal and choroidal stratification Vascular slabs Vitreous humor Retina Inner retina

Superficial vascular complex (SVC)

Deep vascular complex (DVC) Avascular complex (AC) Choroidal layer (Choroid)

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Radial peripapillary capillary plexus (RPCP) Superficial vascular plexus (SVP) Intermediate capillary plexus (ICP) Deep capillary plexus (DCP) Pigment epithelial detachment (PED) Choroicapillaris layer (CCL) Medium and large choroidal vessels

b

Upper boundary Top of the data ILM-5 μm

Lower boundary ILM-5 μm NFL/GCL

NFL/GCL

Lower 1/3 of the GCL+IPL complex INL layer 1/2

Lower 1/3 of the GCL+IPL complex INL layer 1/2 INL/OPL+25 μm RPE BM-10 μm BM+25 μm

INL/OPL+25 μm RPE BM-10 μm BM+25 μm Subchoroidal border

c

Fig. 2.8  Quantitative analysis of blood flow. (a) FAZ analysis; (b) blood flow density; (c) blood flow perfusion area

Manual Slab En face imaging, thickness analysis, and OCTA imaging all rely on accurate segmentation of the retinal and choroid. For severe lesions, there is a risk of partial identification bias in artificial intelligence segmentation. To ensure the accuracy of the en face image rendering and quantification results, the accuracy of layer segmentation should be checked. If the default slab is incorrect, it should be adjusted promptly. In the retinal avascular complex shown in Fig.  2.9a, there is an abnormal blood flow signal in the en face image, which is suspected to be neovascularization, and the corresponding B-scan below also shows the blood flow signal in the corresponding layer. However, if we study it ourselves, we can see that the lower border of the retinal avascular layer has partially crossed into the choriocapillary layer at this location, which causes the blood

flow signal from the choriocapillary layer to enter the retinal avascular layer, thus making the blood flow signal from the choriocapillaries appear in the upper blood flow en face image. To determine whether neovascularization is present at this location, the segmentation is modified by the manual segmentation function. By modifying the segmentation of one B-scan image in this area, the modified result (Fig. 2.9b) is automatically propagated by algorithm. Now we found that the blood flow signal in the upper blood flow en face image disappeared, the corresponding B-scan segmentation in the lower boundary is correct, and the lower boundary stratification is close to Bruch’s membrane, so we could conclude that the abnormal blood flow signal in Fig. 2.9a was caused by the segmentation error. Figure 2.10 shows the interface for manual layer adjustment. In area 1, drag the green line to select the B-scan with the wrong segmentation, in area 2, select the layer interface

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a

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b

Fig. 2.9  Retinal avascular layer blood flow en face image. (a) Incorrect BM slab, resulting in a portion of the retinal avascular layer image containing blood flow signal from the choriocapillary; (b) modified BM slab, retinal avascular layer blood flow signal is correctly displayed

to be modified, and in area 3, modify the blue segmentation line with the mouse. When the modification is completed, the manually corrected segmentation of the current B-scan will be automatically extended to the surrounding area. Continue to select the next B-scan to be modified in area 3, and so on. When all is done, click on the “Save” button on the right and the van Gogh software will apply the new segmentation, reprocess the flow images, thickness analysis images, en face images, etc. Please refer to the device manual for detailed operation.

Scientific Research Function In addition to the above common clinical analysis functions, van Gogh software also provides some advanced analysis functions for scientific needs, such as imaging and quantification of large vessels in the choroid. A trained AI model can be used to identify the location of large vessels in the choroid and perform OCT 3D reconstruction to quantify the morphology of these large vessels based on the B-scans. A specific example is given in Figs. 2.11, 2.12 and 2.13.

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Fig. 2.10  Adjusted stratification lines are shown in blue in the artificial intelligence stratification modification

Fig. 2.11  Volume of large vascular lumen in choroid

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Fig. 2.12  Choroidal vascularity index (CVI): ratio of luminal area to the total choroidal area

Fig. 2.13  Calculation of volume and area of subretinal fluid

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Swept-Source OCT of Normal Eyes Jingyuan Yang, Erqian Wang, and Youxin Chen

OCT and OCTA provide noninvasive, high-resolution, and volumetric images of fundus structures and blood flow. Compared with conventional spectrum domain (SD) OCT and OCTA, SS-OCT and OCTA have advantages of faster speed, broader scanning range, and better penetrability, and thus can display fundus structure and blood flow of larger area and better depth. SS-OCT can clearly show the posterior vitreous, retina, choroid, and even the sclera and post-scleral tissues, as well as reconstruct 3D model of these tissues. A single scanning of SS-OCTA can cover an area of at least 12 mm × 12 mm with a resolution of up to 1024 pixels × 1024 pixels.

SS-OCT B-Scan of Normal Eyes SS-OCT can clearly visualize vitreous structures, including the posterior vitreous cortex, the anterior macular vitreous cavity, and other liquefied vitreous cavities, as well as the

retina and choroid, and the outer boundary of choroid (Figs. 3.1 and 3.2). Sometimes SS-OCT can show the sclera and its internal short posterior ciliary artery course and posterior scleral tissue. The choroid could be divided into four layers, which are Bruch’s membrane, choriocapillary layer, the Sattler’s layer (of vessels of medium diameter), and Haller’s layer (of vessels of large diameter) from inside to outside [1]. Bruch’s membrane is an extremely thin matrix rich in collagen fibers and elastic fibers, with a thickness of 2–4 μm. The choroidal capillary layer is composed of dense capillary tissue, and its lumen is slightly wider than that of ordinary capillaries, and the endothelial cells of the vessels are rich in pore windows. Histological studies have shown that the choriocapillary layer is about 10 μm thick at birth and becomes progressively thinner with age. The Sattler’s layer and the Haller’s layer are mainly composed of medium and large vessels, respectively. There is no clear boundary between these two layers. It has been shown that the aver-

J. Yang · E. Wang · Y. Chen (*) Department of Ophthalmology, Peking Union Medical College Hospital, Beijing, China e-mail: [email protected]

© Scientific and Technical Documentation Press 2023 Y. Chen, X. Peng (eds.), Atlas of Swept Source OCT and OCT Angiography, https://doi.org/10.1007/978-981-19-4391-1_3

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Fig. 3.1  16 mm SS-OCT wide-field single-line scan. In eyes without posterior vitreous detachment, the posterior vitreous cortex shows slightly high signal in front of temporal retina and near the optic disc, while the rest of the vitreous cavity shows no signal. The retinal and choroidal layers are clearly visible:internal limiting membrane, retinal nerve fiber layer, ganglion cell layer, inner plexiform layer, inner nuclear layer, outer plexiform layer, outer nuclear layer, external limit-

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ing membrane, myoid zone, ellipsoid zone, interdigitation zone, retinal pigment epithelium–Bruch’s membrane complex, choriocapillary layer, Sattler’s layer, Haller’s layer, the boundary between choroid and sclera, and some intra-scleral structures, such as the deep scleral stripes beneath the temporal boundary of choroid and sclera, which suggest intra-scleral vessels. A cross-section of the blood vessels in the optic cup can be seen, with shadowing of obscured hyporeflection

age thickness Sattler’s layer of foveal in healthy subjects (Fig. 3.3) is 87 ± 56 μm, and the average thickness of the foveal Haller’s layer is 141 ± 50 μm [2]. The thickness of the choroid is related to racial, age, axial length of eye ball, measurement location, and even scanning time, and the thickness varies widely among population. The foveal choroidal thickness in middle-aged adults is generally considered to be in the range of 200–300 μm. A good depth of field ensures that the OCT images of eyes with long eye axial length do not have foldover artifacts (Fig. 3.4). The B-scan clearly shows not only the retinal and choroidal structures but also the vitreous and vitreous liquefaction cavity anteriorly (vertical arrow), and the sclera, the short intra-scleral posterior ciliary artery (asterisk), and even part of the post-scleral tissue posteriorly (horizontal arrow). Fig. 3.2  6 mm depth SS-OCT wide-field single-line scan. The retina and choroid are normal in the macula. With the increased range of scanning depth, more vitreous structure can be detected, with the presence of hyporeflective cavities in the vitreous (*), the temporal posterior vitreous cortex showing a striated texture almost perpendicular to the retina, and the cortex between the two cavities showing a texture almost parallel to the retina, with a slightly higher density in front of the optic disc (arrow)

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a

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Fig. 3.3  SS-OCT images of a 40-year-old healthy emmetropic woman. Scan mode: Single-line HD OCT. (a) The choroidal thickness under the central macular area is 285 μm; (b) B-scan clearly shows the structure

of the choroidal layers. (1) RPE-Bruch’s membrane complex; (2) choriocapillary layer; (3) Sattler’s layer; (4) Haller’s layer; (5) choroidal-­ scleral interface

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Fig. 3.4  SS-OCT image of a 28-year-old male with myopia. Equivalent spherical refraction −5.50 DS, axial length 26.34  mm; scan mode: Single-line HD OCT

En Face SS-OCT of Normal Eyes A-Scan for Commercial SS-OCT The A-scan speed of commercial SS-OCT devices can reach up to 200,000 scans/s. If the scan range remains the same, the SS-OCT scan density can be increased significantly to obtain volumetric information of the scan area.

Fig. 3.5  Wide-field en face SS-OCT retinal nerve fiber layer image. The scanning area is 12 mm × 12 mm, and the resolution is 1024 pixels × 1024 pixels. Wispy retinal nerve fiber reflex signal can be seen emanating from the optic disc, which is divided by horizontal plane. The retinal vascular signals travel almost in the same direction as the fibers of the retinal nerve fiber layer

En Face OCT En face OCT, also known as C-scan in some studies, is a kind of OCT image paralleled to the retinal surface by processing the 3D data, which can clearly show the retinal nerve fiber alignment, retinal and choroidal macrovascular alignment (Figs. 3.5 and 3.6), and lesions such as cystoid edema, exudate, and tissue defects. En face OCT images and B-scan OCT images complemented each other to show fundus structures (Fig. 3.7).

 uantitative Analysis of SS-OCT Volumetric Q Information SS-OCT can also be used to quantify volumetric information, such as the average thickness and volume of the retinal layers, pigment epithelial detachment, and choroid in a given area, as well as the 3D choroidal blood flow index (i.e., the volumetric percentage of choroidal vessels in choroid) (Figs. 3.8 and 3.9). It is now possible to reconstruct retinal and choroidal structures (including SVision devices) based on SS-OCT volumetric information (Fig. 3.10).

Fig. 3.6  Wide-field en face SS-OCT image of the choroid layer. The scan area is 12 mm × 12 mm, and the resolution is 1024 pixels × 1024 pixels. Choroidal vessels of low signal can be seen, and translucent retinal vascular shadows of medium and large diameter are also detected

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Fig. 3.7  Comparison of choroidal en face OCT and B-scan OCT in normal subjects of different age groups (upper: 20 years old; middle: 40 years old; lower: 60 years old). The en face OCT image shows that

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the choroidal vessels become thinner and steeper in diameter with age. The B-scan OCT images showed that the choroidal thickness become progressively thinner with age

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Fig. 3.8 SS-OCT quantitative analysis of thickness. Scan range 3 mm × 3 mm, resolution 512 pixels × 512 pixels. The left column is the thickness pseudo-color map, and the right column is the average thick-

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ness value (μm) in the 1 mm × 1 mm grid for quantitative analysis. The layers of quantitative thickness analysis are retina, ganglion cell layer, and choroid layer from top to bottom

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a

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Fig. 3.9  Wide field SS-OCT quantitative analysis of choroidal vascular index. Scanning range 12  mm  ×  12  mm, resolution 1024 pixels × 1024 pixels. Pseudo-color map of choroidal vascular index (a), as

 S-OCT Quantitative Analysis of Optic Disc S Correlation SS-OCT can also display en face images of the optic disc region and perform quantitative analysis of optic disc-related metrics, such as thickness of various layers (Figs. 3.11 and 3.12).

well as its ETDRS grid (b), 1 mm × 1 mm grid (c) and 3 mm × 3 mm grid area (d) were used to quantify the choroidal vascular index

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Fig. 3.10  SS-OCT reconstruction of the retina and choroid in the macula in three dimensions (grayscale image). Scanning range 3 mm × 3 mm, resolution 512 pixels × 512 pixels. The image can be

a

viewed in any angle by 3D rotation, and the retinal layers and choroidal structures can be observed in three dimensions

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Fig. 3.11  En face image of nerve fiber layer in optic disc region. The scanning range is 6 mm × 6 mm, with a resolution of 512 pixels × 512 pixels. The nerve fiber layer en face image (a) corresponds to the retinal

layer between the two cyan lines in the B-scan image (b); its thickness can be displayed not only as a specific value in ETDRS partitions (c) but also as a thickness pseudo-color map (d)

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Fig. 3.12  En face image of the optic disc region from the ganglion cell layer to the inner plexiform layer. Scan range 6 mm × 6 mm, resolution 512 pixels × 512 pixels. The level en face image (a) corresponds to the

SS-OCTA Images of Normal Eyes Retina The retina is one of the most oxygen-consuming tissues in the body, so it is important to study the retina by observing the retinal vasculature. The blood supply to the eye is provided by the ophthalmic artery, a branch of the internal carotid artery. The ophthalmic artery has several branches, including the central retinal artery and the short posterior ciliary artery. The short posterior ciliary artery, usually 15–20 in number, passes through the sclera surrounding the optic nerve

retinal level between the two cyan lines in the B-scan image (b); its thickness is shown in ETDRS grid with specific values (c) and thickness pseudo-color maps (d)

and enters the choroid, branching out in a dendritic pattern to form the outer choroidal arteriole, which ends in the choriocapillary. The choriocapillaris are a continuous layer of anastomosing capillary beds, with capillary walls rich in window pores and capillary diameters of about 20–25 μm. The postcapillary venules anastomose with each other to form medium-sized vessels in the Sattler’s layer and large vessels in the Haller’s layer, but there is no clear anatomical boundary between these two layers. The small retinal veins collect blood from the capillaries and converge into the central retinal vein, which leaves the eyeball by the optic nerve, parallel to the central retinal artery. It is now believed that the retinal vessels supply the inner 2/3 of the retina and the choroidal vessels supply the

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outer 1/3 of the retina. The fine structure of the retinal vessels meets both the nutritional requirements and the optical properties of the retina. There is an avascular zone in the fovea, which is surrounded by a continuous ring of capillaris. In the nerve fiber layer, capillaries travel along the axons of the retinal ganglion cells and branch out continuously, forming a dense vascular network in the retinal ganglion cell layer. The space between capillaries is small to reduce the distance of oxygen diffusion and to increase the partial pressure of oxygen in the retina. Most commercial OCTA systems classify the retina into superficial retinal capillary network, deep retinal capillary network, outer avascular retinal layer, and choroidal capillary layer, and the nomenclature may vary slightly among manufacturers. Previous histological studies have confirmed that retinal capillaries can be divided into three layers: superficial, intermediate, and deep layers, which are located in the retinal nerve fiber layer, adjacent to the inner boundary of the inner nuclear layer and adjacent to the outer plexiform layer, respectively. The commercial OCTA system combines the intermediate and outer layers into a deep layer according to the characteristics of the image, but there are differences between the depth of the superficial and deep layers and their definitions. Currently, it is believed that the capillary ring around the foveal avascular zone belongs to the deep retinal capillaries. Some commercial OCTA systems can provide more layers of images, such as the vitreous layer, the overall retinal layer, the pigment epithelial detachment layer, and the choroidal layer (Fig.  3.13). Currently, wide-field OCTA images that are synthesized by automatically combing multiple images together with specific algorithm often provide more information on blood flow (Fig. 3.14).

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Choroid The structure of the choroid is rich in blood flow, but the characteristics of blood flow within the choroidal vessels are very different from that of the retina. Animal studies have shown that blood flow in the choroidal capillaries is four times slower than that in the retinal vessels, while blood flow in the large choroidal vessels is ten times faster than that in the retina [3, 4]. Since red blood cell flow is the basis of OCTA images, the effect of blood flow velocity should be fully considered when interpreting choroidal OCTA images. SVision OCTA defines the choroidal capillary layer as the area from 10  μm above Bruch’s membrane to 25  μm below Bruch’s membrane. OCTA images of the choroidal capillary layer had uniformly distributed black and white dotted, snowflake-like, or granular appearance. In this layer, the choroidal capillaries are close to each other and disorganized, and the movement of red blood cells is slow. Therefore, in the choroidal capillary layer, the location of a white pixel in the OCTA image does not mean that there is exactly one capillary, but only that the contrast signal of the movement of multiple erythrocytes from the vicinity is detected. The location of a black pixel in the OCTA image does not necessarily mean that there is no capillary there, but that the flow rate of nearby red blood cells is slow, or that the motion contrast signal is not strong enough. Therefore, OCTA images of the choroidal capillary layer do not accurately show the capillary location, but only irregular black-and-white dot-like signals. The choroid layer is defined by SVision OCTA as 25 μm below Bruch’s membrane to the lower border of the Choroid. Although the signal loss in the deep choroid tissue using this

Retinal superficial capillary plexus layer

Fig. 3.13  Wide-field SS-OCTA image. The scan area is 12 mm × 12 mm, resolution 1024 pixels × 1024 pixels. The en face OCTA image, the en face OCT image and the corresponding B-scan OCTA image of various levels (marked by cyan lines) are shown in each row from left to right

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43 Retinal Deep capillary plexus layer

Retinal Avascular Complex layer

Choriocapillary layer

Fig. 3.13 (continued)

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Choroid layer

Posterior vitreous layer

Fig. 3.13 (continued)

OCTA device is less than that of other devices, the projection artifacts generated by the choriocapillary layer still have a significant impact on the imaging of the underlying choroid vessels. Therefore, the quality of OCTA images of the large choroid vessels are often unsatisfying. Clear imaging of the choroidal vessels can be achieved by en face OCT images. En face OCT is a flattened imaging method, based on the B-scan reconstruction and calculation of structural OCT to obtain a flattened image. The advantage of SVision OCT for deep choroidal tissue imaging is that in en face OCT images of large choroidal vessels, vessels can be seen very clearly. By inverting the en face OCT image in black and white, a white image of the choroidal vessels can be obtained, which is similar to the early indocyanine green angiography image of the choroidal vessels, but with clearer and more detailed vessel borders. The human choroid is a complex vascular tissue, and OCTA and en face OCT images of choroidal vessels were constructed at different choroidal depths to show the choroidal vascular network at different depths (Fig.  3.15). Each individual’s choroidal vascular network structure is unique

and has similar recognition properties as fingerprints (Figs. 3.16 and 3.17). The vascular arches of the superficial retinal capillary network in OCTA images have several branches which connect to each other and form a reticular vascular plexus. The superficial capillary network also has branches that extend deeper and form a radial plexus at the terminals, which intersect with each other to form a macular ring in the fovea (the ring is now considered to be part of the deep retinal capillary layer), and the foveal avascular area located in it (Fig. 3.18). The larger arterioles of the retina are often accompanied by avascular areas that are not easily noticed, whereas the venules are not, and can be used to identify retinal arterioles. OCTA images of the choriocapillary layer are not yet able to resolve the fine vascular network (Figs.  3.19 and 3.20). OCTA images with different scan ranges and resolutions can help the reviewer to identify focal details or to interpret the whole picture. OCTA can provide a variety of quantitative indicators to assess fundus blood flow, including blood flow (perfusion) density, vessel length density, vessel fractal dimension, non-­

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a

b

c

d

Fig. 3.14  Ultra-wide field SS-OCTA montage of retinal layer. (a, b) Automatic montage of OCTA images with a scanning range of 12  mm  ×  12  mm (two eyes from different patients). The wide-field OCTA scan range exceeds that of a single 50° color image, providing

more information. (c) The latest Automatic montage of five OCTA images with a scanning range of 26  mm  ×  21  mm, which provide a broader range of about 200°. (d) The ultra-wide field en face OCT montage of choroid layer clearly shows vortex vein

perfusion area, FAZ perimeter, FAZ area, and FAZ circularity. Most studies have concluded that the values of the same indexes of different commercial OCTA systems are not interchangeable, and the values vary among different age groups, genders and collective states, so the normal range and c­ linical significance of the above indexes need to be further explored. SS-OCTA can also display blood flow information around the optic disc region, not only at the vitreous, retinal, and choroidal levels (Fig. 3.21) but also at the superficial retinal capillary network, the deep retinal capillary network, the outer avascular layer of the retina, and the choriocapillary layer corresponding to the macular scan procedure

(Fig.  3.22), which helps to generate larger scans of OCTA images that include the macula and optic disc region (Figs. 3.23 and 3.24). OCTA can provide a variety of quantitative indicators to assess fundus blood flow, including blood flow (perfusion) density, vessel length density, vessel fractal dimension, non-­ perfusion area, FAZ perimeter, FAZ area, and FAZ circularity. Most studies have concluded that the values of the same indexes of different commercial OCTA systems are not interchangeable, and the values vary among different age groups, genders, and collective states, so the normal range and clinical significance of the above indexes need to be further explored.

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b

c

d

Fig. 3.15  OCTA and en face OCT images of the choroid of a 39-year-­ old male. Scan range 12 mm × 12 mm, resolution 1024 pixels × 1024 pixels. (a) Choroidal capillary layer OCTA image; (b) choroidal layer

OCTA image; (c) choroidal layer en face OCT image (original image); (d) choroidal layer en face OCT image (inverted color image)

3  Swept-Source OCT of Normal Eyes Fig. 3.16  OCTA (left column) and en face OCT (right column) images of the choroidal layer in the left eye of a 55-year-old healthy male. The choroidal thickness under the fovea was 210 μm, and the imaging depths from top to bottom were 50 μm, 100 μm, 150 μm, and 200 μm below Bruch’s membrane, and the large choroidal vessels were shown as dark stripes

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48 Fig. 3.17  OCTA (left column) and en face OCT (right column) images of the choroidal layer in the right eye of a 48-year-old healthy male. The choroidal thickness under the fovea is 344 μm, and the imaging depths from top to bottom are 50 μm, 100 μm, 150 μm, 200 μm, 250 μm, and 300 μm below Bruch’s membrane. The large choroidal vessels are shown as dark streaks

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3  Swept-Source OCT of Normal Eyes

Fig. 3.18  SS-OCTA images of the superficial (left) and deep (right) capillary plexus layers of the retina. The scan area was 3 mm × 3 mm, and the resolution was 512 pixels × 512 pixels. The capillaries in the superficial and deep layers of the retina are different, and the deep capil-

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lary network shows a clear arch ring border. The deep retinal capillary network showed a sea-snake head-like radial pattern in many places, suggesting the existence of a traffic branch with the superficial retinal capillary network in the center of the capillary plexus (45715195)

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a

b

c

d

Fig. 3.19  Macular center SS-OCTA image. Scan range 3 mm × 3 mm, resolution 512 pixels × 512 pixels. SS-OCTA images of the superficial (a) and deep (b) capillary layers of the retina, retinal layer (c, superficial + deep layers) and choriocapillary layer images (d). It shows that the overlap of both superficial and deep layers of retinal capillaries is the

retinal layer image. The image of the choroidal capillary layer showed a snowflake pattern, and it is difficult to identify the capillary pattern, and faint slightly large vascular shadow could be seen locally, with scattered dotted and lamellar low signal areas (20200110011)

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a

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Fig. 3.20 SS-OCTA images of the macular region. Scan area 6 mm × 6 mm, resolution 512 pixels × 512 pixels. SS-OCTA images of the superficial (a) and deep (b) capillary layers of the retina, retinal vascular images (c, superficial + deep), and choriocapillary layer

images (d). A larger area can be observed than the scan area of 3 mm × 3 mm. The choriocapillary layer is difficult to identify the vascular pattern

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a

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c

Fig. 3.21  Optic disc SS-OCTA image. Scanning range 6 mm × 6 mm, resolution 512 pixels × 512 pixels. SS-OCTA images of blood flow at the vitreous (a), retinal (b), and choroidal (c) levels in the optic disc region

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Fig. 3.22  SS-OCTA images of the superficial (a) and deep (b) capillary layers of the retina, and images of the avascular layer (c) and choroidal capillary layer (d), with different vascular morphology

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Fig. 3.23  Choroidal thickness and blood flow index measurements in 35-year-old woman. The scanning range is 12 mm × 12 mm, and the resolution is 1024 pixels × 1024 pixels. The choroidal thickness measurement provides a complete set of structural and blood flow information for each layer of the retina and choroid within 12 mm × 12 mm of the posterior

pole. One of the features of the functional analysis is the choroidal thickness analysis (a) and the thermogram display (b). (c, d) The built-in software automatically identifies the choroid-scleral interface at each scanning position and calculates choroidal thickness in a defined area at each measurement point and the special features accordingly

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Fig. 3.24  Measurement of choroidal vascular index. Scanning range 12 mm × 12 mm, resolution 1024 pixels × 1024 pixels OCTA. Choroidal vascular index calculation, Another analysis feature of this scanning mode is the choroidal vascular index (CVI) calculation. The built-in software automatically identifies the choroidal vascular lumen (orange

References 1. Ramrattan RS, van der Schaft TL, Mooy CM, et al. Morphometric analysis of Bruch’s mmbrane, the choriocapillaris, and the choroid in aging. Invest Ophthalmol Vis Sci. 1994;35:2857–64. 2. Esmaeelpou M, Kajic V, Zabihian B, et  al. Choroidal Haller’s and Sattler’s layer thickness measurement using 3-dimensional

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area) at each scan location by means of artificial intelligence and calculates the CVI for each measurement point and specific area, i.e. the ratio of choroidal vascular volume in a specific space to the total choroidal volume in that space. The choroidal flow index can be obtained both directly (top left) and visually through a heat map (bottom left)

1060-­ nm optical coherence tomography. PLoS One. 2014;9: e99690. 3. Braun RD, Dewhirst MW, Hatchell DL.  Quantification of erythrocyte flow in the choroid of the albino rat. Am J Physiol. 1997;272:H1444–53. 4. Friedman E, Kopald HH, Smith TR.  Retinal and choroidal blood flow determined with krypton-85 anesthetized animals. Invest Ophthalmol Vis Sci. 1964;3:539–47.

4

Vitreous-Related Disease Chenxi Zhang, Mingzhen Yuan, and Youxin Chen

The vitreous humor is the main component of the refractive-­ media, which transmits light. A viscoelastic gel, the vitreous, supports the retina, cushions external forces, and resists vibration. Blood-vitreous barrier, also known as the retina-­ vitreous barrier can prevent macromolecules in the retinal vessels from entering the vitreous gel. With the development of ophthalmic examination techniques, vitreous diseases have been studied in-depth in the past 20  years. As a high-­resolution, noncontact, noninvasive biologic imag-

ing technique, SS-OCT is increasingly used in clinical and basic medicine to improve our understanding of its pathophysiology.

Posterior Vitreous Detachment See Fig. 4.1.

a

b

Fig. 4.1  Posterior vitreous detachment. Adhesion between the posterior vitreous cortex and the retina is well displayed (a, white arrows). (a) Partial posterior vitreous detachment occurring outside the fovea; (b) Partial posterior vitreous detachment occurring outside the macula region

C. Zhang · M. Yuan · Y. Chen (*) Department of Ophthalmology, Peking Union Medical College Hospital, Beijing, China e-mail: [email protected] © Scientific and Technical Documentation Press 2023 Y. Chen, X. Peng (eds.), Atlas of Swept Source OCT and OCT Angiography, https://doi.org/10.1007/978-981-19-4391-1_4

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 osterior Vitreous Lamellar Fibers P (Horizontal) See Fig. 4.2.

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Centripetal Vitreous Fibers (Vertical) See Fig. 4.3.

Fig. 4.2  Posterior vitreous lamellar fibers (horizontal). Tractional fibers between posterior vitreous cortex and optic disc, which are almost parallel, or at a certain angle to the retina

Fig. 4.3  Centripetal vitreous fibers (vertical). The morphology and borders of centripetal vitreous fiber perpendicular to the retina can be clearly observed, which may be associated with the occurrence of retinoschisis or changes in the shape of eyeballs

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Vitreous Hemorrhage See Fig. 4.4. a

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c

d

Fig. 4.4  Vitreous hemorrhage. SS-OCT clearly shows vitreous hemorrhage as scattered hyperreflective spots in the posterior pole of the vitreous. (a) Vitreous hemorrhage secondary to high myopia; (b) vitreous

hemorrhage of unknown origin; (c) vitreous hemorrhage secondary to diabetic retinopathy; (d) vitreous hemorrhage secondary to retinal macroaneurysm

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Posterior Vitreous Cortex Thickening

Posterior Cortical Vitreous Traction

See Fig. 4.5.

See Figs. 4.6 and 4.7.

Fig. 4.5  Thickening of the posterior vitreous cortex. The cortex of the posterior vitreous detachment is significantly thickened, presenting hyperreflectivity

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Fig. 4.6  Posterior cortical vitreous traction secondary to diabetic retinopathy. A 64-year-old female with diabetic retinopathy. cSSO (a) and B-scan OCT (d, e, f ) reveals posterior cortical vitreous traction at the

bifurcation of the inferotemporal retinal vein in the right eye, and OCTA reveals the tractioned retinal vessels (c). Three-dimensional image shows the posterior vitreous cortical traction from an overall perspective (b)

Fig. 4.7  Posterior cortical vitreous traction secondary to retinal vasculitis. A 33-year-old female with retinal vasculitis. B-scan OCT reveals posterior cortical vitreous traction on the inferotemporal retinal vein of the right eye (arrow)

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Posterior Precortical Vitreous Pocket (PPVP) See Fig. 4.8.

a

Fig. 4.8  Posterior precortical vitreous pocket. The posterior precortical vitreous pocket appears as boat-shaped lacunae in the posterior pole (b, dashed frame) with an anterior border of vitreous humor and a thin

b

layer of vitreous cortex in proximity to the retina; sometimes, a channel connecting the Cloquet canal to the posterior precortical vitreous pocket can be seen near the posterior precortical vitreous pocket (a, arrow)

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 itreous Opacity with Posterior Vitreous V Detachment See Fig. 4.9.

Fig. 4.9 Vitreous opacity with posterior vitreous detachment. A 33-year-old woman with high myopia, presented with an uneven reflection of vitreous cortex with posterior vitreous detachment on B-scan OCT (6 mm in depth)

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Vitreomacular Traction Syndrome Vitreomacular traction syndrome (VMT) is a condition in which persistent vitreomacular attachment leads to excessive traction on the macula during the progression of posterior vitreous detachment, resulting in anatomic changes of the fovea, (e.g. intraretinal pseudocyst, elevation of the outer retina of the fovea) and vision decrease. Common symptoms of vitreomacular traction include metamorphopsia, photopsia, and vision loss. The International Vitreomacular Traction Study Group classifies vitreomacular traction as focal (≤1500 μm) or broad (>1500 μm) based on the diameter of vitreomacular adhesion in OCT. SS-OCT can clearly show the extent of vitreomacular traction and its effect on retinal morphology, such as intraretinal pseudocyst and elevation of the fovea from the RPE in a larger scanning range, which is useful for the follow-up of vitreomacular traction (Fig. 4.10).

Fig. 4.10  SS-OCT shows vitreomacular traction (triangle) and the formation of intraretinal pseudocyst (*) in the fovea

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Epiretinal Membrane

Clinical Grading System

Epiretinal membrane (ERM) is a fibrocellular proliferative disease at the vitreoretinal interface that involves the macula. ERM is classified into two types, idiopathic and secondary ERM.  Secondary ERM is often associated with ocular inflammatory diseases, retinal vascular diseases, and retinal detachment. The majority of ERM cases are idiopathic, with prevalence ranging from 1.02% to 28.9% [1]. Age is recognized as the most important risk factor for idiopathic ERM. Depending on the severity of the disease, patients with ERM maybe asymptomatic, or may have varying degrees of visual distortion, vision loss, or macropsia.

In 1997, Gass classified idiopathic ERM into three grades based on the clinical manifestations, and this grading system is still in clinic use [2] (Fig. 4.11). Grade 0: cellophane maculopathy, the membrane is transparent without distortion of the underlying retina. Only cellophane light reflex is seen ophthalmoscopically (Fig. 4.11a). Grade 1: Crinkled cellophane maculopathy, contraction of the membrane leads to irregular wrinkling of the inner retinal layers and fine superficial radiating folds are often observed (Fig. 4.11b).

a

b

c

Fig. 4.11  cSSO imaging and SS-OCT of different grades of ERM. (a) Cellophane maculopathy (Grade 0), SS-OCT shows ERM with almost normal macular morphology; (b) Crinkled cellophane maculopathy (Grade 1), SS-OCT shows the flattening of fovea and irregular wrin-

kling of the inner retinal layer on the nasal side of the macula; (c) Preretinal macular fibrosis (Grade2), SS-OCT shows the traction of ERM leads to full-thickness retinal distortion and macular edema

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Grade 2: Preretinal macular fibrosis, also known as macular pucker, is characterized by a thicker and more opaque membrane that obscures underlying retinal vessels and tracts the full-thickness retina into distortion. It can be accompanied by retinal edema, hemorrhage, cotton wool spots, and hard exudates (Fig. 4.11c).

SS-OCT and SS-OCTA Compared with SD-OCT, SS-OCT can not only clearly show the range of ERM and its effects on the neural retina in a larger scanning area but also reveal the role that vitreous

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plays, such as posterior vitreous detachment and posterior precortical vitreous pocket (PPVP) in the formation of ERM.  It has been suggested that the posterior wall of the PPVP remains attached to the macula during the progression of posterior vitreous detachment, providing a collagen scaffold for cell proliferation in ERM [3] (Fig.  4.12). Furthermore, SS-OCTA can provide a better visualization of the changes in macular capillary plexuses to assess the severity of ERM and predict the prognosis of ERM surgery. In patients with ERM, the area of fovea avascular zone (FAZ) in superficial capillary plexus was significantly reduced and correlated with the central foveal thickness (CFT) [4–6] (Fig. 4.13).

Fig. 4.12  SS-OCT showing ERM with flattening of fovea. The thickening of posterior wall of posterior_precortical_ vitreous_pocket (P, gray dashed area) forms ERM (arrowheads) and the vitreous remains attached to the macula without significant posterior vitreous detachment

Fig. 4.13  SS-OCTA (12 mm * 12 mm, retina layer) and SS-OCT of ERM. SS-OCTA shows the retinal vessels are deformed and distorted by the traction of ERM, and FAZ is reduced. SS-OCT shows the neural retina in fovea is significantly thickening by the traction of ERM

4  Vitreous-Related Disease

Macular Hole Macular hole (MH) is a break in the neurosensory retinal layer of fovea. Most cases of macular hole are idiopathic, but it can also be secondary to ocular blunt trauma, laser injury, high myopia, foveoschisis, and intraocular surgery. Abnormal vitreomacular traction during the progression of posterior vitreous detachment is regarded as an important cause of idiopathic macular hole. Vision loss, metamorphopsia, and central scotoma

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are common symptoms of macular hole, and the WatzkeAllen test can be positive.

Clinical Staging In 2013, the International Vitreomacular Traction Study (IVTS) proposed an OCT-based classification system for macular hole [7], which correlates with commonly used Gass macular hole stages as following: (Fig. 4.14, Table 4.1).

a

b

c

d

Fig. 4.14  cSSO imaging and SS-OCT of macular hole. (a) Gass stage 1 macular hole, SS-OCT shows vitreomacular traction, elevation of the fovea, and formation of intraretinal pseudocysts. Small irregular elevation of retinal pigment epithelium underlying the fovea representing drusen is also observed; (b) Gass stage 2 macular hole, SS-OCT shows a full-thickness macular hole with an aperture about 400 μm, the operculum is still attached to retina with vitreomacular traction and mild

intraretinal cysts. (c) Gass stage 3 macular hole, SS-OCT shows a full-­ thickness macular hole with cystoid macular changes. The aperture is about 490  μm, and the operculum is detached from the retina and adhered to the posterior vitreous cortex; (d) Gass stage 4 macular hole, SS-OCT shows a full-thickness macular hole with mild cystoid changes in macula, the aperture is about 600 μm. The operculum is not present and vitreous is fully separated from macula

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SS-OCT and SS-OCTA SS-OCT can clearly show the traction of the posterior wall of posterior precortical vitreous pocket on fovea in macular hole formation (Fig.  4.15). And compared to SD-OCT, SS-OCT does better in visualization of the macular struc-

tures in gas-filled eyes, facilitating treatment plan making in early postoperative stage, such as the choice of postoperative posture [8] (Fig. 4.16). SS-OCTA can evaluate the macular capillary plexuses, helpful to monitor the anatomic and functional changes in macular hole (Fig. 4.17).

Table 4.1  Gass macular hole stages and International Vitreomacular Traction Study (IVTS) classification system Gass stages Stage 1 Figure 4.14a

Stage 2 Figure 4.14b Stage 3 Figure 4.14c Stage 4 Figure 4.14d

Gass attributes Impending macular hole, yellow foveal spot or ring, and elevation of fovea is above RPE Full-thickness macular hole (≤400 μm)

Full-thickness macular hole (>400 μm)with partial vitreomacular traction Full-thickness macular hole with posterior vitreous detachment

IVTS classification system Only vitreomacular traction

Small (≤250 μm) or medium (>250 μm ~ ≤400 μm) full-thickness macular hole with vitreomacular traction Large (>400 μm) full-­ thickness macular hole with vitreomacular traction Small, medium, or large full-thickness macular hole without vitreomacular traction

Fig. 4.15  SS-OCT of Gass stage 3 macular hole. SS-OCT shows a Gass stage 3 full-thickness macular hole with cystoid macular changes (*). The operculum (arrowhead) is adhered to the posterior wall of posterior precortical vitreous pocket(P)

a

b

Fig. 4.16  Comparison of preoperative and postoperative SS-OCT images of idiopathic macular hole. (a) Preoperative SS-OCT showed a Gass stage 2 macular hole with its operculum attached to retina and

cystoid macular changes. (b) 1 week after vitrectomy, SS-OCT showed the macular hole had been closed up, but the ellipsoid zone underlying the fovea had not been fully recovered

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a

b

Fig. 4.17  Comparison of preoperative and postoperative SS-OCT and SS-OCTA images of macular hole. (a) Preoperative SS-OCT showed Gass stage 3 macular hole and SS-OCTA (3 mm × 3 mm, inner retina) showed increased FAZ and decreased perifoveal vessel density. Cystoid

References 1. Bu S, Kuijer R, Li X, Hooymans J, Los L.  Idiopathic epiretinal membrane. Retina. 2014;34(12):2317–35. 2. Gass JDM.  Macular dysfunction caused by epiretinal membrane contraction. In: Stereoscopic atlas of macular diseases: diagnosis and treatment, vol 2. 4th ed. St Louis, MO: Mosby; 1997. p. 938–50. 3. Lavinsky F, Lavinsky D. Novel perspectives on swept-source optical coherence tomography. Int J Retina Vitreous. 2016;2:25. 4. Kim YJ, Kim S, Lee JY, Kim JG, Yoon YH. Macular capillary plexuses after epiretinal membrane surgery: an optical coherence tomography angiography study. Br J Ophthalmol. 2017;102(8):1086–91.

macular changes were observed in en face image. (b) Postoperative SS-OCT showed healing of the macular hole, SS-OCTA (6 mm × 6 mm, inner retina) shows a decrease in FAZ and disappearance of cystic changes in en face image

5. Kumagai K, Furukawa M, Suetsugu T, Ogino N. Foveal avascular zone area after internal limiting membrane peeling for epiretinal membrane and macular hole compared with that of fellow eyes and healthy controls. Retina. 2018;38(9):1786–94. 6. Kitagawa Y, Shimada H, Shinojima A, Nakashizuka H. Foveal avascular zone area analysis using optical coherence tomography angiography before and after idiopathic epiretinal membrane surgery. Retina. 2019;39(2):339–46. 7. Duker J, Kaiser P, Binder S, et al. The international vitreomacular traction study group classification of vitreomacular adhesion, traction and macular hole. Ophthalmology. 2013;120(12):2611–9. 8. Ahn SJ, Park SH, Lee BR. visualization of the macula in gas-filled eyes: spectral domain optical coherence tomography versus swept-­ source optical coherence tomography. Retina. 2018;38(3):480–9.

5

Pathologic Myopia Mingzhen Yuan and Youxin Chen

At present, there are about 600 million people with myopia in China, and about 30  million with high myopia, and the number is still growing. Among them, it is important to note that more than 40% of high myopia will progress to pathologic myopia (PM), the incidence of which is increasing year by year and tends to be younger [1]. PM is usually defined as a refractive error of greater than −6.0 diopters or an axial length greater than 26.5  mm, combined with characteristic degenerative changes in ocular fundus lesions in the posterior pole. The typical fundus lesions include tessellated fundus, lacquer cracks, diffuse or lamellar atrophy, choroidal neovascularization, macular atrophy, and posterior scleral staphyloma [2]. In 2015, international scholars developed a more simplified META-PM (meta-analysis of pathologic myopia) study classification standard, which classifies myopic macular degeneration into five categories: no myopic retinopathy (Category 0), fundus changes only (Category 1), diffuse chorioretinal atrophy (Category 2), patchy chorioretinal atrophy (Category 3), and macular atrophy (Category 4). In addition to this classification, three additional features were included as “plus signs,” namely, lacquer cracks, choroidal neovascularization (CNV), and Fuchs’ spots. Myopic maculopathy includes diffuse chorioretinal atrophy, patchy chorioretinal atrophy, lacquer cracks, myopic CNV, and CNV-related macular atrophy. And the definition of pathologic myopia has recently shifted to “the presence of myopic maculopathy

equal to or more severe than diffuse chorioretinal atrophy [3]. OCT, as a high-resolution, noncontact, noninvasive biological tissue imaging technique, is increasingly used in clinical and basic medicine to improve our understanding of the development of some diseases. With the continuous development of OCT-related technology, its scanning speed is becoming faster, scanning depth is deepening, scanning resolution is increasing, and scanning range is expanding, these advantages are of great significance for the diagnosis of PM-related complications, enabling clinicians to have a more comprehensive understanding of PM, which is of great significance for the diagnosis, treatment, and followup of PM [4].

Retinal Lesions In clinical practice, common pathologic myopia retina-­ related lesions include myopic traction maculopathy (MTM), macular hole, retinal hole, subretinal hemorrhage, outer retinopathy, epiretinal membrane, peripapillary retinal cavitation, peripapillary retinoschisis, and retinal pigment epithelial rupture, etc [5]. This section will summarize the SS-OCT-related manifestations of pathologic myopic retina-related lesions (Table  5.1, Figs.  5.1, 5.2, 5.3, 5.4, 5.5, 5.6, 5.7, 5.8, 5.9, and 5.10).

M. Yuan · Y. Chen (*) Department of Ophthalmology, Peking Union Medical College Hospital, Beijing, China e-mail: [email protected] © Scientific and Technical Documentation Press 2023 Y. Chen, X. Peng (eds.), Atlas of Swept Source OCT and OCT Angiography, https://doi.org/10.1007/978-981-19-4391-1_5

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Myopic Tractional Macular Degeneration (MTM) Table 5.1  SS-OCT characteristics of pathologic myopia-associated retinal lesions Classification S0 MTM

Appearance No retinoschisis

S1 MTM

Extrafoveal macular retinoschisis

S2 MTM

Foveal macular retinoschisis

Figures

SS-OCT HD Single-Line Scan

SS-OCT HD Single-Line Scan

SS-OCT HD Single-Line Scan

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5  Pathologic Myopia Table 5.1 (continued) Classification S3 MTM

Appearance Both foveal and extrafoveal but not the entire macula retinoschisis

S4 MTM

Entire macula retinoschisis

Figures

SS-OCT HD Single-Line Scan

SS-OCT HD Single-Line Scan

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Macular Hole

Fig. 5.1  SS-OCT wide-field high definition (HD) single-line scan (depth 6 mm). SS-OCT has the advantage of being more comprehensive and precise in the diagnosis of myopic traction maculopathy due to its deeper image range and wider field

a

c

b

d

Fig. 5.2  SS-OCT single-line scan. OCT has an irreplaceable role in the diagnosis of macular hole and can clearly display full thickness macular hole (b), lamellar macular hole (a, c), and macular hole combined with other fundus lesions (d)

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Retinal Break

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Subretinal Hemorrhage

Fig. 5.3  SS-OCT wide-angle high-resolution single-line scan (6 mm in scan depth). Two full thickness retinal breaks can be observed (dashed box)

Fig. 5.4  SS-OCT multiline scan. A 33-year-old male with high myopia (SE in left eye −10.50 DS). SS-OCT clearly shows subretinal hemorrhage as shown in the dashed box, which exhibits as a homogeneous moderate reflective signal

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Outer Retinopathy

Fig. 5.5  SS-OCT wide-angle single-line scan. A 24-year-old female with high myopia (SE in both eyes −11.00DS). The disruption of ellipsoid zone is clearly shown in the dashed box

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Epiretinal Membrane

a

b Fig. 5.6  SS-OCT wide-field single-line scan. Local preretinal fibroproliferative membrane stretching retina is clearly visible in macular area (dashed box), which can cause retinoschisis (a) We can see the shape of the epiretinal membrane more clearly (b)

Fig. 5.7  SS-OCT wide-angle single-line scan. A 23-year-old male with high myopia (SE in right eye −8.00DS). The preretinal fibroproliferative membrane stretching retina is clearly shown in front of the optic disc (dashed box)

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Peripapillary Retinal Cavitation

Fig. 5.8  SS-OCT wide-field single-line scan. A cyst-like retinal hyporeflective signal (arrow) is display next to the optic disc; this presentation may be an early manifestation of peripapillary retinoschisis

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Peripapillary Retinoschisis

Fig. 5.9  SS-OCT wide-angle single-line scan. Retinoschisis located adjacent to the optic disc (dashed box)

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Retinal Pigment Epithelial Tear

Fig. 5.10  SS-OCT single-line scan. A 35-year-old male with high myopia (SE in left eye −12.00 DS), localized retinal pigment epithelial tear is clearly shown in macular area (dashed box)

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Choroidal and Bruch’s Membrane Lesions Pathologic myopia is often combined with choroid-related lesions, the most common of which is choroidal neovascularization (CNV) [6]. With the development of OCT device, the ability to distinguish Bruch’s membrane is gradually improving, thus enabling physician to get a more comprehensive understanding of myopic Bruch’s membrane-related lesions.

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This section will summarize the SS-OCT and OCTA manifestations of choroidal and Bruch’s membrane-associated lesions in pathologic myopia (Figs.  5.11, 5.12, 5.13, 5.14, 5.15, 5.16, 5.17, 5.18, 5.19, 5.20, and 5.21).

Choroidal Neovascularization

Fig. 5.11  SS-OCT high-resolution single-line scan and SS-OCTA images. B-scan SS-OCT shows a homogeneous hyperreflective subretinal bulge with Bruch’s membrane depression; 3 mm × 3 mm SS-OCTA shows intertwined neovascularization with a clear border

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Fig. 5.12  SS-OCT HD single-line scan and SS-OCTA images. B-scan SS-OCT shows uneven reflection of the lesion and a tendency of thinning of the choroid; 3 mm × 3 mm SS-OCTA shows that the neovascu-

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larization had begun to degenerate, the structure was sparse, and the border was blurred

Fig. 5.13  SS-OCT high-resolution single-line scan and SS-OCTA images. B-scan SS-OCT shows choroidal atrophy under the CNV and Bruch’s membrane; 3 mm × 3 mm SS-OCTA shows large abnormal blood vessels

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Intrachoroidal Cavitation

Fig. 5.14  SS-OCT single-line scan. The choroidal thickness is thin next to the optic disc, and a hyporeflective cavity-like structure is shown adjacent to the optic disc, indicating the accumulation of fluid in the cavity

Fig. 5.15  SS-OCT multilinear scan. The intrachoroidal cavitation may also be in the macular area, with multifocal hyporeflective cavity-like structures (arrows) visible under the macular fovea

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Choroidal Atrophy

Fig. 5.16  SS-OCT multiline scan. The cSSO image shows early focal atrophy of the choroid, corresponding to the choroidal defect on B-scan OCT (dashed box)

Fig. 5.17  SS-OCT wide-angle high-resolution single-line scan. The area of choroidal atrophy is enlarged, and the lesions of atrophy are further expanded (dashed box)

Fig. 5.18  SS-OCT single-line scan. The area of choroidal atrophy is expanding, the lesions are fusing, and the choroid is gradually thinning and atrophying (dashed box)

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Uneven Diameter of Choroidal Vessels

a

b

c

d

Fig. 5.19  SS-OCT wide-field single-line scan. The choroidal Haller layer is unevenly sized (dashed box), and the dilated Haller layer sometimes communicates with the scleral vessels to form penetrating branches (a, b). Local dilation of the choroidal Haller or Sattler layer (c, d)

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Bruch’s Membrane Depression, Fracture

Fig. 5.20  SS-OCT Wide-field single-line scan. Bruch’s membrane depression and choroidal atrophy are visible under the fovea (dashed box)

a

b Fig. 5.21  SS-OCT wide-field HD single-line scan. Localized Bruch’s membrane breaks and depressions are seen in the macular area (a–b, dashed box), and scleral depressions may sometimes be combined at the lesion (a)

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Sclera-Related Lesions In recent years, with the increasing depth, resolution and scope of OCT scans, sclera-related lesions have attracted more and more attention from clinicians [7]. In patients with pathologic myopia, sclera-related lesions, such as scleral depression, scleral splitting, and scleral penetrating

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vessels, are easily formed due to the growing eye axis. Therefore, this section will summarize the SS-OCT findings of scleral-­ related lesions in pathologic myopia (Figs. 5.22, 5.23, and 5.24).

Scleral Depression

a Fig. 5.22  SS-OCT HD single-line scan. SS-OCT is taken at a deeper depth, allowing a comprehensive analysis of the corresponding lesions in the sclera of PM patients; as shown in the dashed box, the sclera is

b

partially depressed, uneven in thickness (a), and often combined with retinoschisis (b)

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Scleral Splitting

a

b

c

d

Fig. 5.23  SS-OCT HD single-line scan. SS-OCT clearly shows post-scleral adipose tissue; as shown in the dashed box, the outer sclera partially splitting, and the splitting space is filled by adipose tissue (a–d); some of these lesions may be combined with CNV (b) and choroidal atrophy (d)

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Scleral Penetration of Blood Vessels

Fig. 5.24  SS-OCT multiline scan. SS-OCT can clearly display the pathway, structure, and morphology of scleral vessels (arrows). Note the dilated scleral blood vessels with uneven shape, which are often associated with other fundus lesions

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Optic Nerve-Related Lesions Myopic optic neuropathy is an important cause of vision loss in patients with pathologic myopia. Unlike macular degeneration, many patients with myopic optic neuropathy experience complete vision loss and central retinal artery occlusion at a younger age than patients with glaucoma, so early detection of optic neuropathy and regular observation of changes

Fig. 5.25  SS-OCT single-line scan. Optic disc pit is round or polygonal in shape, often covered by a grayish fibrous membrane, and is usually seen on the temporal or inferior temporal side of the optic disc.

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in optic neuropathy are essential in patients with pathologic myopia [8]. This section will summarize the OCT findings of optic nerve-related lesions in pathologic myopia (Figs. 5.25, 5.26, and 5.27).

Optic Disc Pit

SS-OCT can visualize the details of the relevant structures of the optic disc (arrows), and can clearly show the structure of the optic disc pit

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Tilted Disc

Fig. 5.26  SS-OCT HD single-line scan. The tilted disc is due to posterior bulging of the eyeball wall in high myopia and the tilting entry of the optic nerve into the eyeball, resulting in one side of the optic disc to

shift backwards (mostly temporal); the shape and structure of the tilted disc can be clearly observed under the horizontal cut of the optic disc

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Deepening of the Optic Cup

Fig. 5.27  SS-OCT wide-field high-resolution single-line scan. A 23-year-old female with high myopia (SE in left eye −12.00DS) and deepening optic cup is clearly shown in the left eye (arrow)

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Other Lesions In addition to the above-mentioned retinal, choroidal, scleral, and optic nerve lesions associated with pathologic myopia, there are other lesions that have received much attention in recent years, such as dome-shaped macula, pos-

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terior scleral staphyloma, and subarachnoid space [9–12]. The OCT manifestations are shown in Figs. 5.28, 5.29, 5.30, 5.31, 5.32, and 5.33.

Dome-Shaped Macula

a

b

Fig. 5.28  SS-OCT circumferential multiline scan. The dome-shaped macula is a special shape of the posterior pole of high myopic, characterized by a partial inward protrusion of the sclera in the macular

area. A vertical dome-shaped macula is an oval dome that appears only vertically (a), not visible in other directions (b), with large scleral vessels visible at the dome macula (dashed box a)

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Fig. 5.29  SS-OCT circumferential multiline scan. Oval domes occur only horizontally

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Fig. 5.30  SS-OCT wide-field high-resolution single-line scan. Oval domes are present in all directions, often combined with retinoschisis (dashed box)

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a

b

c Fig. 5.31  SS-OCT Circular multilinear scan. No ovoid domes appear horizontally or vertically (b, c), and only in a particular direction (a)

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Posterior Staphyloma

a

b

c Fig. 5.32  SS-OCT single-line scan and circular multiline scan. Posterior scleral staphyloma is a complication of high myopia, where the elongation of the eye axis causes the posterior sclera to be thin and protrude posteriorly to form a depression. As shown by the red arrow, the sclera bulges posteri-

orly in the posterior scleral staphyloma, the corresponding choroid is thinned by the pressure. (a) limited macular posterior scleral staphyloma; (b) posterior scleral staphyloma around the optic disc; (c) posterior scleral staphyloma in the macular area on B-scan SS-OCT (scan depth of 6 mm)

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Subarachnoid Cavity

LC

OF

SAS

ON

Fig. 5.33  SS-OCT circumferential multiline scan. The optic nerve is surrounded by cerebrospinal fluid, and sometimes the subarachnoid space terminates in the posterior wall of the eye at the scleral rim, and

can therefore be visualized on OCT. The red line shows the subarachnoid space, which is a normal tissue structure and is useful for determining scleral curvature and other related pathologies

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References 1. Cicinelli MV, Pierro L, Gagliardi M, et  al. Optical coherence tomography and pathological myopia: an update of the literature. Int Ophthalmol. 2015;35(6):897–902. 2. Fan H, Ma H, Gao R, et  al. Associated factors for visibility and width of retrobulbar subarachnoid space on swept-source optical coherence tomography in high myopia. Sci Rep. 2016;6:36723. 3. Shinohara K, Moriyama M, Shimada N, et  al. Characteristics of peripapillary staphylomas associated with high myopia determined by swept-source optical coherence tomography. Am J Ophthalmol. 2016;169:138–44. 4. Ng DSC, Cheung CYL, Luk FO, et al. Advances of optical coherence tomography in myopia and pathologic myopia. Eye (Lond). 2016;30(7):901–16. 5. Schaal KB, Pang CE, Pozzoni MC, et al. The premacular bursa’s shape revealed in vivo by swept-source optical coherence tomography. Ophthalmology. 2014;121(5):1020–8. 6. Ishida T, Watanabe T, Yokoi T, et al. Possible connection of short posterior ciliary arteries to choroidal neovascularisations in eyes with pathologic myopia. Br J Ophthalmol. 2019;103(4):457–62.

95 7. Ohno-Matsui K, Jonas JB, Spaide RF.  Macular Bruch membrane holes in choroidal neovascularization-related myopic macular atrophy by swept-source optical coherence tomography. Am J Ophthalmol. 2016;162:133–9. 8. Pan T, Su Y, Yuan ST, et al. Optic disc and peripapillary changes by optic coherence tomography in high myopia. Int J Ophthalmol. 2018;11(5):874–80. 9. Gal-Or O, Freund KB.  Multimodal imaging findings in dome-­ shaped macula. Ophthalmology. 2017;124(3):335. 10. Liang IC, Shimada N, Tanaka Y, et al. Comparison of clinical features in highly myopic eyes with and without a dome-shaped macula. Ophthalmology. 2015;122(8):1591–600. 11. Ohsugi H, Ikuno Y, Oshima K, et al. Morphologic characteristics of macular complications of a dome-shaped macula determined by swept-source optical coherence tomography. Am J Ophthalmol. 2014;158(1):162–70, e171. 12. Ohno-Matsui K, Jonas JB.  Posterior staphyloma in pathologic myopia. Prog Retin Eye Res. 2019;70:99–109.

6

Age-Related Macular Degeneration Xinyu Zhao, Mingyue Luo, and Youxin Chen

Age-related macular degeneration (AMD) is a group of age-­ related macular diseases induced by a variety of factors. The common characteristic of AMD is the pathological changes in the nutritional structure of macular like retinal pigment epithelium (RPE) and choroid, which might lead to visual impairment and progressive loss of central vision [1–3]. The disease can be divided into two basic subtypes: neovascular AMD (wet) and non-neovascular AMD (dry). The main characteristic of nAMD is the formation of abnormal neovascularization from choroid (CNV). Due to functional and structural abnormalities, these CNVs will lead to hemorrhage, edema, and fibrosis under and intra the retina, then resulting in rapid loss of central vision. Dry AMD is relatively common, manifested as progressive loss of photoreceptor cells and aggravative geographic atrophy. The vision loss of dry AMD patients is relatively slow, but there is no effective treatment at present, thus regular follow-up is usually adopted to prevent the transformation from dry AMD to nAMD.

X. Zhao · M. Luo · Y. Chen (*) Department of Ophthalmology, Peking Union Medical College Hospital, Beijing, China e-mail: [email protected]

Non-neovascular AMD The Age-Related Eye Disease Study (AREDS) divides the AMD as follows [4] (Figs. 6.1, 6.2, and 6.3): Non-AMD (AREDS 1): The control group in AREDS, with no or only very small drusen (