Artifacts and artifact removal in optical coherence tomographic angiography

Optimization of handheld spectrally encoded coherence tomography and reflectometry for point-of-care ophthalmic diagnostic imaging

Significance

Handheld optical coherence tomography (HH-OCT) systems enable point-of-care ophthalmic imaging in bedridden, uncooperative, and pediatric patients. Handheld spectrally encoded coherence tomography and reflectometry (HH-SECTR) combines OCT and spectrally encoded reflectometry (SER) to address critical clinical challenges in HH-OCT imaging with real-time en face retinal aiming for OCT volume alignment and volumetric correction of motion artifacts that occur during HH-OCT imaging.

Aim

We aim to enable robust clinical translation of HH-SECTR and improve clinical ergonomics during point-of-care OCT imaging for ophthalmic diagnostics.

Approach

HH-SECTR is redesigned with (1) optimized SER optical imaging for en face retinal aiming and retinal tracking for motion correction, (2) a modular aluminum form factor for sustained alignment and probe stability for longitudinal clinical studies, and (3) one-handed photographer-ergonomic motorized focus adjustment.

Results

We demonstrate an HH-SECTR imaging probe with micron-scale optical-optomechanical stability and use it for in vivo human retinal imaging and volumetric motion correction.

Conclusions

This research will benefit the clinical translation of HH-SECTR for point-of-care ophthalmic diagnostics.

Vascular feature identification in actinic keratosis grades I-III using dynamic optical coherence tomography with automated, quantitative analysis

Clinical grading of actinic keratosis (AK) is based on skin surface features, while subclinical alterations are not taken into consideration. Dynamic optical coherence tomography (D-OCT) enables quantification of the skin´s vasculature, potentially helpful to improve the link between clinical and subclinical features. We aimed to compare microvascular characteristics across AK grades using D-OCT with automated vascular analysis. This explorative study examined AK and photodamaged skin (PD) on the face or scalp. AKs were clinically graded according to the Olsen Classification scheme before D-OCT assessment. Using an open-source software tool, the OCT angiographic analyzer (OCTAVA), we quantified vascular network features, including total and mean vessel length, mean vessel diameter, vessel area density (VAD), branchpoint density (BD), and mean tortuosity from enface maximum intensity projection images. Additionally, we performed subregional analyses on selected scans to overcome challenges associated with imaging through hyperkeratosis (each lesion group; n = 18). Our study included 45 patients with a total of 205 AKs; 93 grade I lesions, 65 grade II, 47 grade III and 89 areas with PD skin. We found that all AK grades were more extensively vascularized relative to PD, as shown by greater total vessel length and VAD (p ≤ 0.009). Moreover, AKs displayed a disorganized vascular network, with higher BD in AK I-II (p < 0.001), and mean tortuosity in AK II-III (p ≤ 0.001) than in PD. Vascularization also increased with AK grade, showing significantly greater total vessel length in AK III than AK I (p = 0.029). Microvascular quantification of AK unveiled subclinical, quantitative differences among AK grades I-III and PD skin. D-OCT-based microvascular assessment may serve as a supplement to clinical AK grading, potentially raising perspectives to improve management strategies.

Optical coherence tomography angiography for the differentiation of diabetic nephropathy from non-diabetic renal disease

BACKGROUND

To provide a new non-invasive method for the differentiation of diabetic nephropathy (DN) from non-diabetic renal disease (NDRD) by assessing retinal microstructure using optical coherence tomography angiography (OCTA).

METHODS

OCTA parameters were recorded and their relationship with DN was analysed. A differential diagnosis regression model for DN was established, and the diagnostic efficiency was evaluated.

RESULTS

Based on the pathological results of renal biopsy, 31 DN patients and 35 NDRD patients were included. Multivariate logistic regression analysis showed that DN was independently associated with the following parameters: 15.3 mm-1 ≤ vessel density (VD) full < 17.369 mm-1 (odds ratio [OR]=8.523; 95% confidence interval [CI]=1.387-52.352; P = 0.021), VD full < 15.3 mm-1 (OR=8.202; 95% CI=1.110-60.623; P = 0.039), DM duration > 60 months (OR=7.588; 95% CI=1.569-36.692; P = 0.012), and estimated glomerular filtration rate < 60 mL/min/1.73 m2 (OR=24.484; 95% CI=4.308-139.142; P < 0.001). The area under the receiver operating characteristic curve was 0.911, indicating a high diagnostic efficiency.

CONCLUSIONS

VD full < 17.369 mm-1, DM duration > 60 months, and eGFR < 60 mL/min/1.73 m2 may indicate the presence of DN. OCTA may be an effective non-invasive method for identifying DN and NDRD.

Imaging Artifacts and Quality Evaluation with Ultrawide-Field Swept-Source OCTA in Diabetic Retinopathy

PURPOSE

To evaluate the prevalence and types of artifacts in ultrawide-field swept-source optical coherence tomography angiography (SS-OCTA) scans of diabetic retinopathy (DR) patients.

METHODS

This study was a prospective, observational study conducted from May 2022 to October 2022. Participants comprised individuals with proliferative diabetic retinopathy (PDR), nonproliferative diabetic retinopathy (NPDR), no diabetic retinopathy, and healthy controls. SS-OCTA imaging was performed, and a 5-scan composite with a larger field of view (23.5 mm × 17.5 mm) was captured using built-in software. Two experienced ophthalmologists analyzed the images independently, and the image quality and artifact prevalence were recorded and analyzed.

RESULTS

The study included 70 eyes (16 with PDR, 24 with NPDR, 12 eyes of diabetic patients without DR, and 18 healthy eyes) in 70 subjects. Imaging artifacts were observed in a high percentage of eyes, with 98.57% of eyes presenting at least one type of artifact. A significant proportion of eyes (58.57%) exhibited a severe degree of artifacts. The most prevalent artifacts were loss of signal in 63 eyes (90%) and displacement artifact and masking artifact in 43 eyes (61.4%). Patients with more severe stages of DR had higher artifact scores (p < 0.05). Multivariate regression analysis indicated that DR severity was the most important factor influencing artifact scores (p < 0.05).

CONCLUSIONS

In OCTA photos, various artifacts arise at different frequencies. It is crucial to qualitatively evaluate the images to ensure their quality. The results demonstrate that DR severity has a significant correlation with artifact scores.

Visualizing features with wide-field volumetric OCT angiography

Optical coherence tomography (OCT) and its extension OCT angiography (OCTA) have become essential clinical imaging modalities due to their ability to provide depth-resolved angiographic and tissue structural information non-invasively and at high resolution. Within a field of view, the anatomic detail available is sufficient to identify several structural and vascular pathologies that are clinically relevant for multiple prevalent blinding diseases, including age-related macular degeneration (AMD), diabetic retinopathy (DR), and vein occlusions. The main limitation in contemporary OCT devices is that this field of view is limited due to a fundamental trade-off between system resolution/sensitivity, sampling density, and imaging window dimensions. Here, we describe a swept-source OCT device that can capture up to a 12 × 23-mm field of view in a single shot and show that it can identify conventional pathologic features such as non-perfusion areas outside of conventional fields of view. We also show that our approach maintains sensitivity sufficient to visualize novel features, including choriocapillaris morphology beneath the macula and macrophage-like cells at the inner limiting membrane, both of which may have implications for disease.

Optimizing numerical k-sampling for swept-source optical coherence tomography angiography

High-quality swept-source optical coherence tomography (SS-OCT) requires accurate k-sampling, which is equally vital for optical coherence tomography angiography (OCTA). Most SS-OCT systems are equipped with hardware-driven k-sampling. However, this conventional approach raises concerns over system cost, optical alignment, imaging depth, and stability in the clocking circuit. This work introduces an optimized numerical k-sampling method to replace the additional k-clock hardware. Using this method, we can realize high axial resolution (4.9-µm full-width-half-maximum, in air) and low roll-off (2.3 dB loss) over a 4-mm imaging depth. The high axial resolution and sensitivity achieved by this simple numerical method can reveal anatomic and microvascular structures with structural OCT and OCTA in both macular and deeper tissues, including the lamina cribrosa, suggesting its usefulness in imaging retinopathy and optic neuropathy.

ARTIFICIAL INTELLIGENCE FOR OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY-BASED DISEASE ACTIVITY PREDICTION IN AGE-RELATED MACULAR DEGENERATION

PURPOSE

The authors hypothesize that optical coherence tomography angiography (OCTA)-visualized vascular morphology may be a predictor of choroidal neovascularization status in age-related macular degeneration (AMD). The authors thus evaluated the use of artificial intelligence (AI) to predict different stages of AMD disease based on OCTA en face 2D projections scans.

METHODS

Retrospective cross-sectional study based on collected 2D OCTA data from 310 high-resolution scans. Based on OCT B-scan fluid and clinical status, OCTA was classified as normal, dry AMD, wet AMD active, and wet AMD in remission with no signs of activity. Two human experts graded the same test set, and a consensus grading between two experts was used for the prediction of four categories.

RESULTS

The AI can achieve 80.36% accuracy on a four-category grading task with 2D OCTA projections. The sensitivity of prediction by AI was 0.7857 (active), 0.7142 (remission), 0.9286 (dry AMD), and 0.9286 (normal) and the specificity was 0.9524, 0.9524, 0.9286, and 0.9524, respectively. The sensitivity of prediction by human experts was 0.4286 active choroidal neovascularization, 0.2143 remission, 0.8571 dry AMD, and 0.8571 normal with specificity of 0.7619, 0.9286, 0.7857, and 0.9762, respectively. The overall AI classification prediction was significantly better than the human (odds ratio = 1.95, P = 0.0021).

CONCLUSION

These data show that choroidal neovascularization morphology can be used to predict disease activity by AI; longitudinal studies are needed to better understand the evolution of choroidal neovascularization and features that predict reactivation. Future studies will be able to evaluate the additional predicative value of OCTA on top of other imaging characteristics (i.e., fluid location on OCT B scans) to help predict response to treatment.

Optical Coherence Tomography Angiography: A 2023 Focused Update on Age-Related Macular Degeneration

Optical coherence tomography angiography (OCTA) has extensively enhanced our comprehension of eye microcirculation and of its associated diseases. In this narrative review, we explored the key concepts behind OCTA, as well as the most recent evidence in the pathophysiology of age-related macular degeneration (AMD) made possible by OCTA. These recommendations were updated since the publication in 2020, and are targeted for 2023. Importantly, as a future perspective in OCTA technology, we will discuss how artificial intelligence has been applied to OCTA, with a particular emphasis on its application to AMD study.

Pupil vignetting artifact on optical coherence tomography angiography

PURPOSE

To discuss the features of an artifact on optical coherence tomography angiography (OCTA), termed "pupil vignetting artifact," and describe how it may masquerade as true chorioretinal pathology.

DESIGN

This was a retrospective, observational case series.

METHODS

The authors studied 12 eyes at a vitreoretinal clinic in Eastern India, reviewing a dark shadow such as an artifact on OCTA images.

RESULTS

In all 12 eyes, there was an appearance of a dark shadow on OCTA imaging, located at the macula, superior, superotemporal, or superonasal to the fovea, which did not correspond to any ischemic area responsible for flow-void or any media opacity casting a posterior shadow. It was believed to be an artifact caused by the vignetting effect of the pupil as the incident OCT beam clips the iris during OCTA scanning, and therefore reduces the amount of total light incident on the retina. The variability in the size, shape, and location of the artifact is contributed by a few factors such as variable angle of incident light on the pupil, pupillary dynamics, and curvature of the retinal surface.

CONCLUSION

Pupil vignetting artifact is a unique undescribed phenomenon appearing at the macula on OCTA imaging that can masquerade as numerous true chorioretinal pathologies. This article aims to describe this artifact to avoid misinterpretation and further confusion in real-life clinical practice.

Imaging microvascular changes in nonocular oncological clinical applications by optical coherence tomography angiography: a literature review

BACKGROUND

Optical coherence tomography angiography (OCTA) is an emerging imaging modality that enables noninvasive visualization and analysis of tumor vasculature. OCTA has been particularly useful in clinical ocular oncology, while in this article, we evaluated OCTA in assessing microvascular changes in clinical nonocular oncology through a systematic review of the literature.

METHOD

The inclusion criterion for the literature search in PubMed, Web of Science and Scopus electronic databases was the use of OCTA in nonocular clinical oncology, meaning that all ocular clinical studies and all ocular and nonocular animal, phantom, ex vivo, experimental, research and development, and purely methodological studies were excluded.

RESULTS

Eleven articles met the inclusion criteria. The anatomic locations of the neoplasms in the selected articles were the gastrointestinal tract (2 articles), head and neck (1 article) and skin (8 articles).

CONCLUSIONS

While OCTA has shown great advancements in ophthalmology, its translation to the nonocular clinical oncology setting presents several limitations, with a lack of standardized protocols and interpretation guidelines posing the most significant challenge.

Optical coherence tomography angiography in diabetic retinopathy

There remain many unanswered questions on how to assess and treat the pathology and complications that arise from diabetic retinopathy (DR). Optical coherence tomography angiography (OCTA) is a novel and non-invasive three-dimensional imaging method that can visualize capillaries in all retinal layers. Numerous studies have confirmed that OCTA can identify early evidence of microvascular changes and provide quantitative assessment of the extent of diseases such as DR and its complications. A number of informative OCTA metrics could be used to assess DR in clinical trials, including measurements of the foveal avascular zone (FAZ; area, acircularity, 3D para-FAZ vessel density), vessel density, extrafoveal avascular zones, and neovascularization. Assessing patients with DR using a full-retinal slab OCTA image can limit segmentation errors and confounding factors such as those related to center-involved diabetic macular edema. Given emerging data suggesting the importance of the peripheral retinal vasculature in assessing and predicting DR progression, wide-field OCTA imaging should also be used. Finally, the use of automated methods and algorithms for OCTA image analysis, such as those that can distinguish between areas of true and false signals, reconstruct images, and produce quantitative metrics, such as FAZ area, will greatly improve the efficiency and standardization of results between studies. Most importantly, clinical trial protocols should account for the relatively high frequency of poor-quality data related to sub-optimal imaging conditions in DR and should incorporate time for assessing OCTA image quality and re-imaging patients where necessary.

OCT angiography and its retinal biomarkers [Invited]

Optical coherence tomography angiography (OCTA) is a high-resolution, depth-resolved imaging modality with important applications in ophthalmic practice. An extension of structural OCT, OCTA enables non-invasive, high-contrast imaging of retinal and choroidal vasculature that are amenable to quantification. As such, OCTA offers the capability to identify and characterize biomarkers important for clinical practice and therapeutic research. Here, we review new methods for analyzing biomarkers and discuss new insights provided by OCTA.

Improved in vivo optical coherence tomography imaging of animal peripheral nerves using a prism nerve holder

Significance

Modern optical volumetric imaging modalities, such as optical coherence tomography (OCT), provide enormous information about the structure, function, and physiology of living tissue. Although optical imaging achieves lateral resolution on the order of the wavelength of light used, and OCT achieves axial resolution on a similar micron scale, tissue optical properties, particularly high scattering and absorption, limit light penetration to only a few millimeters. In addition, in vivo imaging modalities are susceptible to significant motion artifacts due to cardiac and respiratory function. These effects limit access to artifact-free optical measurements during peripheral neurosurgery to only a portion of the exposed nerve without further modification to the procedure.

Aim

We aim to improve in vivo OCT imaging during peripheral neurosurgery in small and large animals by increasing the amount of visualized nerve volume as well as suppressing motion of the imaged area.

Approach

We designed a nerve holder with embedded mirror prisms for peripheral nerve volumetric imaging as well as a specific beam steering strategy to acquire prism and direct view volumes in one session with minimal motion artifacts.

Results

The axially imaged volumes from mirror prisms increased the OCT signal intensity by > 22    dB over a 1.25-mm imaging depth in tissue-mimicking phantoms. We then demonstrated the new imaging capabilities in visualizing peripheral nerves from direct and side views in living rats and minipigs using a polarization-sensitive OCT system. Prism views have shown nerve fascicles and vasculature from the bottom half of the imaged nerve which was not visible in direct view.

Conclusions

We demonstrated improved OCT imaging during neurosurgery in small and large animals by combining the use of a prism nerve holder with a specifically designed beam scanning protocol. Our strategy can be applied to existing OCT imaging systems with minimal hardware modification, increasing the nerve tissue volume visualized. Enhanced imaging depth techniques may lead to a greater adoption of structural and functional optical biomarkers in preclinical and clinical medicine.

Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications

Artificial intelligence (AI) has been approved for biomedical research in diverse areas from bedside clinical studies to benchtop basic scientific research. For ophthalmic research, in particular glaucoma, AI applications are rapidly growing for potential clinical translation given the vast data available and the introduction of federated learning. Conversely, AI for basic science remains limited despite its useful power in providing mechanistic insight. In this perspective, we discuss recent progress, opportunities, and challenges in the application of AI in glaucoma for scientific discoveries. Specifically, we focus on the research paradigm of reverse translation, in which clinical data are first used for patient-centered hypothesis generation followed by transitioning into basic science studies for hypothesis validation. We elaborate on several distinctive areas of research opportunities for reverse translation of AI in glaucoma including disease risk and progression prediction, pathology characterization, and sub-phenotype identification. We conclude with current challenges and future opportunities for AI research in basic science for glaucoma such as inter-species diversity, AI model generalizability and explainability, as well as AI applications using advanced ocular imaging and genomic data.

Quantitative approaches in multimodal fundus imaging: State of the art and future perspectives

When it first appeared, multimodal fundus imaging revolutionized the diagnostic workup and provided extremely useful new insights into the pathogenesis of fundus diseases. The recent addition of quantitative approaches has further expanded the amount of information that can be obtained. In spite of the growing interest in advanced quantitative metrics, the scientific community has not reached a stable consensus on repeatable, standardized quantitative techniques to process and analyze the images. Furthermore, imaging artifacts may considerably affect the processing and interpretation of quantitative data, potentially affecting their reliability. The aim of this survey is to provide a comprehensive summary of the main multimodal imaging techniques, covering their limitations as well as their strengths. We also offer a thorough analysis of current quantitative imaging metrics, looking into their technical features, limitations, and interpretation. In addition, we describe the main imaging artifacts and their potential impact on imaging quality and reliability. The prospect of increasing reliance on artificial intelligence-based analyses suggests there is a need to develop more sophisticated quantitative metrics and to improve imaging technologies, incorporating clear, standardized, post-processing procedures. These measures are becoming urgent if these analyses are to cross the threshold from a research context to real-life clinical practice.

Deep-Learning-Aided Diagnosis of Diabetic Retinopathy, Age-Related Macular Degeneration, and Glaucoma Based on Structural and Angiographic OCT

Purpose

Timely diagnosis of eye diseases is paramount to obtaining the best treatment outcomes. OCT and OCT angiography (OCTA) have several advantages that lend themselves to early detection of ocular pathology; furthermore, the techniques produce large, feature-rich data volumes. However, the full clinical potential of both OCT and OCTA is stymied when complex data acquired using the techniques must be manually processed. Here, we propose an automated diagnostic framework based on structural OCT and OCTA data volumes that could substantially support the clinical application of these technologies.

Design

Cross sectional study.

Participants

Five hundred twenty-six OCT and OCTA volumes were scanned from the eyes of 91 healthy participants, 161 patients with diabetic retinopathy (DR), 95 patients with age-related macular degeneration (AMD), and 108 patients with glaucoma.

Methods

The diagnosis framework was constructed based on semisequential 3-dimensional (3D) convolutional neural networks. The trained framework classifies combined structural OCT and OCTA scans as normal, DR, AMD, or glaucoma. Fivefold cross-validation was performed, with 60% of the data reserved for training, 20% for validation, and 20% for testing. The training, validation, and test data sets were independent, with no shared patients. For scans diagnosed as DR, AMD, or glaucoma, 3D class activation maps were generated to highlight subregions that were considered important by the framework for automated diagnosis.

Main Outcome Measures

The area under the curve (AUC) of the receiver operating characteristic curve and quadratic-weighted kappa were used to quantify the diagnostic performance of the framework.

Results

For the diagnosis of DR, the framework achieved an AUC of 0.95 ± 0.01. For the diagnosis of AMD, the framework achieved an AUC of 0.98 ± 0.01. For the diagnosis of glaucoma, the framework achieved an AUC of 0.91 ± 0.02.

Conclusions

Deep learning frameworks can provide reliable, sensitive, interpretable, and fully automated diagnosis of eye diseases.

Financial Disclosures

Proprietary or commercial disclosure may be found after the references.

Longitudinal characterization of retinal vasculature alterations with optical coherence tomography angiography in a mouse model of tauopathy

Tauopathies are a family of neurodegenerative diseases which predominately afflict the rapidly growing aging population suffering from various brain disorders including Alzheimer's disease, frontotemporal dementia with parkinsonism-17 and Pick disease. As the only visually accessible region of the central nervous system, in recent years, the retina has attracted extensive attention for its potential as a target for visualizing and quantifying emerging biomarkers of neurodegenerative diseases. Our previous study has found that retinal vascular inflammation and leakage occur at the very early stage of tauopathic mouse model. Here, we aimed to non-invasively visualize age-dependent alterations of retinal vasculature assessing the potential for using changes in retinal vasculature as the biomarker for the early diagnosis of tauopathy. Optical coherence tomography angiography (OCTA), a non-invasive depth-resolved high-resolution imaging technique was used to visualize and quantify tauopathy-induced alterations of retinal vasculature in P301S transgenic mice overexpressing the P301S mutant form of human tau and age-matched wild type littermate mice at 3, 6 and 10 months of age. We observed significant alterations of vascular features in the intermediate capillary plexus (ICP) and deep capillary plexus (DCP) but not in the superficial vascular complex (SVC) of P301S mice at early stages of tauopathy. With aging, alterations of vascular features in P301S mice became more prominent in all three vascular plexuses. Staining of retinal vasculature in flatmounts and trypsin digests of P301S mice at 10 months of age revealed decreased vessel density and increased acellular capillary formation, indicating that vascular degeneration also occurs during tauopathy. Overall, our results demonstrate that the changes in retinal vascular features accelerate during the progression of tauopathy. Vessels in the ICP and DCP may be more susceptible to tauopathy than vessels in the SVC. Since changes in retinal vasculature often precede tau pathology in the brain, non-invasive identification of retinal vascular alterations with OCTA may be a useful biomarker for the early diagnosis of tauopathy and monitoring its progression.

Towards standardizing retinal optical coherence tomography angiography: a review

The visualization and assessment of retinal microvasculature are important in the study, diagnosis, monitoring, and guidance of treatment of ocular and systemic diseases. With the introduction of optical coherence tomography angiography (OCTA), it has become possible to visualize the retinal microvasculature volumetrically and without a contrast agent. Many lab-based and commercial clinical instruments, imaging protocols and data analysis methods and metrics, have been applied, often inconsistently, resulting in a confusing picture that represents a major barrier to progress in applying OCTA to reduce the burden of disease. Open data and software sharing, and cross-comparison and pooling of data from different studies are rare. These inabilities have impeded building the large databases of annotated OCTA images of healthy and diseased retinas that are necessary to study and define characteristics of specific conditions. This paper addresses the steps needed to standardize OCTA imaging of the human retina to address these limitations. Through review of the OCTA literature, we identify issues and inconsistencies and propose minimum standards for imaging protocols, data analysis methods, metrics, reporting of findings, and clinical practice and, where this is not possible, we identify areas that require further investigation. We hope that this paper will encourage the unification of imaging protocols in OCTA, promote transparency in the process of data collection, analysis, and reporting, and facilitate increasing the impact of OCTA on retinal healthcare delivery and life science investigations.

Real-time OCT image denoising using a self-fusion neural network

Optical coherence tomography (OCT) has become the gold standard for ophthalmic diagnostic imaging. However, clinical OCT image-quality is highly variable and limited visualization can introduce errors in the quantitative analysis of anatomic and pathologic features-of-interest. Frame-averaging is a standard method for improving image-quality, however, frame-averaging in the presence of bulk-motion can degrade lateral resolution and prolongs total acquisition time. We recently introduced a method called self-fusion, which reduces speckle noise and enhances OCT signal-to-noise ratio (SNR) by using similarity between from adjacent frames and is more robust to motion-artifacts than frame-averaging. However, since self-fusion is based on deformable registration, it is computationally expensive. In this study a convolutional neural network was implemented to offset the computational overhead of self-fusion and perform OCT denoising in real-time. The self-fusion network was pretrained to fuse 3 frames to achieve near video-rate frame-rates. Our results showed a clear gain in peak SNR in the self-fused images over both the raw and frame-averaged OCT B-scans. This approach delivers a fast and robust OCT denoising alternative to frame-averaging without the need for repeated image acquisition. Real-time self-fusion image enhancement will enable improved localization of OCT field-of-view relative to features-of-interest and improved sensitivity for anatomic features of disease.

Pulsatile tissue deformation dynamics of the murine retina and choroid mapped by 4D optical coherence tomography

Irregular ocular pulsatility and altered mechanical tissue properties are associated with some of the most sight-threatening eye diseases. Here we present 4D optical coherence tomography (OCT) for the quantitative assessment and depth-resolved mapping of pulsatile dynamics in the murine retina and choroid. Through a pixel-wise analysis of phase changes of the complex OCT signal, we reveal spatiotemporal displacement characteristics across repeated frame acquisitions. We demonstrate in vivo fundus elastography (FUEL) imaging in wildtype mouse retinas and in a mouse model of retinal neovascularization and uncover subtle structural deformations related to ocular pulsation. Our data in mouse eyes hold promise for a powerful retinal elastography technique that may enable a new paradigm of OCT-based measurements and image contrast.

An Open-Source Deep Learning Network for Reconstruction of High-Resolution OCT Angiograms of Retinal Intermediate and Deep Capillary Plexuses

Purpose

We propose a deep learning-based image reconstruction algorithm to produce high-resolution optical coherence tomographic angiograms (OCTA) of the intermediate capillary plexus (ICP) and deep capillary plexus (DCP).

Methods

In this study, 6-mm × 6-mm macular scans with a 400 × 400 A-line sampling density and 3-mm × 3-mm scans with a 304 × 304 A-line sampling density were acquired on one or both eyes of 180 participants (including 230 eyes with diabetic retinopathy and 44 healthy controls) using a 70-kHz commercial OCT system (RTVue-XR; Optovue, Inc., Fremont, California, USA). Projection-resolved OCTA algorithm removed projection artifacts in voxel. ICP and DCP angiograms were generated by maximum projection of the OCTA signal within the respective plexus. We proposed a deep learning-based method, which receives inputs from registered 3-mm × 3-mm ICP and DCP angiograms with proper sampling density as the ground truth reference to reconstruct 6-mm × 6-mm high-resolution ICP and DCP en face OCTA. We applied the same network on 3-mm × 3-mm angiograms to enhance these images further. We evaluated the reconstructed 3-mm × 3-mm and 6-mm × 6-mm angiograms based on vascular connectivity, Weber contrast, false flow signal (flow signal erroneously generated from background), and the noise intensity in the foveal avascular zone.

Results

Compared to the originals, the Deep Capillary Angiogram Reconstruction Network (DCARnet)-enhanced 6-mm × 6-mm angiograms had significantly reduced noise intensity (ICP, 7.38 ± 25.22, P < 0.001; DCP, 11.20 ± 22.52, P < 0.001), improved vascular connectivity (ICP, 0.95 ± 0.01, P < 0.001; DCP, 0.96 ± 0.01, P < 0.001), and enhanced Weber contrast (ICP, 4.25 ± 0.10, P < 0.001; DCP, 3.84 ± 0.84, P < 0.001), without generating false flow signal when noise intensity lower than 650. The DCARnet-enhanced 3-mm × 3-mm angiograms also reduced noise, improved connectivity, and enhanced Weber contrast in 3-mm × 3-mm ICP and DCP angiograms from 101 eyes. In addition, DCARnet preserved the appearance of the dilated vessels in the reconstructed angiograms in diabetic eyes.

Conclusions

DCARnet can enhance 3-mm × 3-mm and 6-mm × 6-mm ICP and DCP angiogram image quality without introducing artifacts.

Translational Relevance

The enhanced 6-mm × 6-mm angiograms may be easier for clinicians to interpret qualitatively.

Signal properties of split-spectrum amplitude decorrelation angiography for quantitative optical coherence tomography-based velocimetry

Split-spectrum amplitude-decorrelation angiography (SSADA) is a noninvasive and three-dimensional angiographic technique with a microscale spatial resolution based on optical coherence tomography. The SSADA signal is known to be correlated with the blood flow velocity and the quantitative velocimetry with SSADA has been expected; however, the signal properties of SSADA are not completely understood due to lack of comprehensive investigations of parameters related to SSADA signals. In this study, phantom experiments were performed to comprehensively investigate the relation of SSADA signals with flow velocities, time separations, particle concentrations, signal-to-noise ratios, beam spot sizes, and viscosities, and revealed that SSADA signals reflect the spatial commonality within a coherence volume between adjacent A-scans.

Detection of the Microvascular Changes of Diabetic Retinopathy Progression Using Optical Coherence Tomography Angiography

Purpose

To investigate microvascular parameters that are related to the severity of diabetic retinopathy (DR) with optical coherence tomography angiography (OCTA).

Methods

In total, 105 eyes from 105 diabetic patients were recruited in this prospective cross-sectional study, including 37 eyes with no clinical signs of DR (NoDR), 43 eyes with nonproliferative diabetic retinopathy (NPDR), and 25 eyes with proliferative diabetic retinopathy (PDR). Angiogram images from the parafoveal superficial capillary plexus (SCP), the deep capillary plexus (DCP), and the radial peripapillary capillary plexus were analyzed, and metrics were compared among groups. Multivariate regression analysis was used to identify the best OCTA parameters that could distinguish DR severity among groups.

Results

Parafoveal vessel diameter index in the SCP and vessel density (VD) in the DCP showed the strongest correlation with the severity of DR (P < 0.01). Extrafoveal avascular area in the SCP was the parameter that could most distinguish NoDR from NPDR (P < 0.01) with sensitivity and specificity of 83.72% and 78.38%, respectively. VD in the DCP also was the most sensitive biomarker to distinguish NPDR from PDR (P < 0.01) with sensitivity and specificity of 84.00% and 79.07%, respectively.

Conclusions

The microvascular changes in the SCP and DCP in DR may have different characteristics that could be identified with specific OCTA parameters. OCTA serves as a promising technology to discriminate eyes with different severity of DR.

Translational Relevance

Our study investigated OCTA metrics and severity of DR. At different stages of DR, ophthalmologists may focus on specific OCTA parameters to predict the progression of retinopathy in individual patients.