Volumetric Optical Coherence Tomography Research Articles

Assessing the efficacy of 2D and 3D CNN algorithms in OCT-based glaucoma detection

Glaucoma is a progressive neurodegenerative disease characterized by the gradual degeneration of retinal ganglion cells, leading to irreversible blindness worldwide. Therefore, timely and accurate diagnosis of glaucoma is crucial, enabling early intervention and facilitating effective disease management to mitigate further vision deterioration. The advent of optical coherence tomography (OCT) has marked a transformative era in ophthalmology, offering detailed visualization of the macula and optic nerve head (ONH) regions. In recent years, both 2D and 3D convolutional neural network (CNN) algorithms have been applied to OCT image analysis. While 2D CNNs rely on post-prediction aggregation of all B-scans within OCT volumes, 3D CNNs allow for direct glaucoma prediction from the OCT data. However, in the absence of extensively pre-trained 3D models, the comparative efficacy of 2D and 3D-CNN algorithms in detecting glaucoma from volumetric OCT images remains unclear. Therefore, this study explores the efficacy of glaucoma detection through volumetric OCT images using select state-of-the-art (SOTA) 2D-CNN models, 3D adaptations of these 2D-CNN models with specific weight transfer techniques, and a custom 5-layer 3D-CNN-Encoder algorithm. The performance across two distinct datasets is evaluated, each focusing on the macula and the ONH, to provide a comprehensive understanding of the models’ capabilities in identifying glaucoma. Our findings demonstrate that the 2D-CNN algorithm consistently provided robust results compared to their 3D counterparts tested in this study for glaucoma detection, achieving AUC values of 0.960 and 0.943 for the macular and ONH OCT test images, respectively. Given the scarcity of pre-trained 3D models trained on extensive datasets, this comparative analysis underscores the overall utility of 2D and 3D-CNN algorithms in advancing glaucoma diagnostic systems in ophthalmology and highlights the potential of 2D algorithms for volumetric OCT image-based glaucoma detection.

Delineation of three-dimensional tumor margins based on normalized absolute difference mapping via volumetric optical coherence tomography

The extent of surgical resection is an important prognostic factor in the treatment of patients with glioblastoma. Optical coherence tomography (OCT) imaging is one of the adjunctive methods available to achieve the maximal surgical resection. In this study, the tumor margins were visualized with the OCT image obtained from a murine glioma model. A commercialized human glioblastoma cell line (U-87) was employed to develop the orthotopic murine glioma model. A swept-source OCT (SS-OCT) system of 1300 nm was used for three-dimensional imaging. Based on the OCT intensity signal, which was obtained via accumulation of each A-scan data, an en-face optical attenuation coefficient (OAC) map was drawn. Due to the limited working distance of the focused beam, OAC values decrease with depth, and using the OAC difference in the superficial area was chosen to outline the tumor boundary, presenting a challenge in analyzing the tumor margin along the depth direction. To overcome this and enable three-dimensional tumor margin detection, we converted the en-face OAC map into an en-face difference map with x- and y-directions and computed the normalized absolute difference (NAD) at each depth to construct a volumetric NAD map, which was compared with the corresponding H&E-stained image. The proposed method successfully revealed the tumor margin along the peripheral boundaries as well as the margin depth. We believe this method can serve as a useful adjunct in glioma surgery, with further studies necessary for real-world practical applications.

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.

Simultaneous alignment and surface regression using hybrid 2D–3D networks for 3D coherent layer segmentation of retinal OCT images with full and sparse annotations

Layer segmentation is important to quantitative analysis of retinal optical coherence tomography (OCT). Recently, deep learning based methods have been developed to automate this task and yield remarkable performance. However, due to the large spatial gap and potential mismatch between the B-scans of an OCT volume, all of them were based on 2D segmentation of individual B-scans, which may lose the continuity and diagnostic information of the retinal layers in 3D space. Besides, most of these methods required dense annotation of the OCT volumes, which is labor-intensive and expertise-demanding. This work presents a novel framework based on hybrid 2D–3D convolutional neural networks (CNNs) to obtain continuous 3D retinal layer surfaces from OCT volumes, which works well with both full and sparse annotations. The 2D features of individual B-scans are extracted by an encoder consisting of 2D convolutions. These 2D features are then used to produce the alignment displacement vectors and layer segmentation by two 3D decoders coupled via a spatial transformer module. Two losses are proposed to utilize the retinal layers’ natural property of being smooth for B-scan alignment and layer segmentation, respectively, and are the key to the semi-supervised learning with sparse annotation. The entire framework is trained end-to-end. To the best of our knowledge, this is the first work that attempts 3D retinal layer segmentation in volumetric OCT images based on CNNs. Experiments on a synthetic dataset and three public clinical datasets show that our framework can effectively align the B-scans for potential motion correction, and achieves superior performance to state-of-the-art 2D deep learning methods in terms of both layer segmentation accuracy and cross-B-scan 3D continuity in both fully and semi-supervised settings, thus offering more clinical values than previous works.

Deep learning network (DL-Net) based classification and segmentation of multi-class retinal diseases using OCT scans

The most frequent cause of blindness in industrialized nations is age-related macular degeneration (AMD), particularly in persons over 60. In recent years in this field, three key factors have led to an increase in the workload of experts and the healthcare system: 1) population aging worldwide prevalence, 2) AMD's chronic nature, and 3) more widespread usage of the retinal optical coherence tomography (OCT) imaging technology. The establishment of fully automated diagnosis systems has recently been made possible by recent developments in the deep learning field. The main objective of the research is to develop a new deep learning-based model using OCT images. The performance for classification and segmentation of retinal diseases is improved by the proposed model through effectively capturing inter-scale features and combining these features using convolutional blocks. Then, we classify volumetric OCT images via a new deep learning-based model. For multi-class classification and segmentation of ocular diseases, the OCT scans of the human eye are used in this research to demonstrate a Deep Learning Network (DL-Net) model by automatically recognizing normal, diabetic macular edema (DME), choroidal neovascularization (CNV), AMD, and drusen images. This research introduces the Modified ResNet-50 approach and Image Processing for multiple OCT image classification. This research presents an efficient diagnostic technique for image segmentation based on a Bi-LSTM-based deep recurrent convolutional neural network (DRCNN). The modified SqueezeNet model analyzes the volumetric segmentation of OCT images. The volumetric publicly available datasets are used to analyze the proposed model. The proposed multi-scale structure model outperforms several well-known OCT classification and segmentation frameworks. 99.76% accuracy is achieved by the proposed model respectively. In healthcare settings, the proposed methodology can be used as a diagnostic instrument to help medical experts make better-diagnosing decisions, as shown by the encouraging proposed architecture's qualitative evaluations and quantitative results by generating a confusion matrix.

Developing a privacy-preserving deep learning model for glaucoma detection: a multicentre study with federated learning

BackgroundDeep learning (DL) is promising to detect glaucoma. However, patients’ privacy and data security are major concerns when pooling all data for model development. We developed a privacy-preserving DL model...

Methods for real-time feature-guided image fusion of intrasurgical volumetric optical coherence tomography with digital microscopy.

4D-microscope-integrated optical coherence tomography (4D-MIOCT) is an emergent multimodal imaging technology in which live volumetric OCT (4D-OCT) is implemented in tandem with standard stereo color microscopy. 4D-OCT provides ophthalmic surgeons with many useful visual cues not available in standard microscopy; however it is challenging for the surgeon to effectively integrate cues from simultaneous-but-separate imaging in real-time. In this work, we demonstrate progress towards solving this challenge via the fusion of data from each modality guided by segmented 3D features. In this way, a more readily interpretable visualization that combines and registers important cues from both modalities is presented to the surgeon.

Live 4D-OCT denoising with self-supervised deep learning

By providing three-dimensional visualization of tissues and instruments at high resolution, live volumetric optical coherence tomography (4D-OCT) has the potential to revolutionize ophthalmic surgery. However, the necessary imaging speed is accompanied by increased noise levels. A high data rate and the requirement for minimal latency impose major limitations for real-time noise reduction. In this work, we propose a low complexity neural network for denoising, directly incorporated into the image reconstruction pipeline of a microscope-integrated 4D-OCT prototype with an A-scan rate of 1.2 MHz. For this purpose, we trained a blind-spot network on unpaired OCT images using a self-supervised learning approach. With an optimized U-Net, only a few milliseconds of additional latency were introduced. Simultaneously, these architectural adaptations improved the numerical denoising performance compared to the basic setup, outperforming non-local filtering algorithms. Layers and edges of anatomical structures in B-scans were better preserved than with Gaussian filtering despite comparable processing time. By comparing scenes with and without denoising employed, we show that neural networks can be used to improve visual appearance of volumetric renderings in real time. Enhancing the rendering quality is an important step for the clinical acceptance and translation of 4D-OCT as an intra-surgical guidance tool.

Comparison of Optical Coherence Tomography Biomarkers between Bevacizumab Good Responders and Nonresponders Who were Switched to Dexamethasone Implant in Diabetic Macular Edema.

To compare volumetric optical coherence tomography (OCT) biomarkers in bevacizumab responsive and bevacizumab refractory diabetic macular edema (DME) patients switched to the dexamethasone implant to ultimately identify possible prognostic indicators. Retrospective analysis of DME patients treated with bevacizumab were done. Patients were divided into those who showed response to bevacizumab (bevacizumab only group) and others who were switched to the dexamethasone implant due to lack of response to bevacizumab (switching group). Volumetric OCT biomarkers such as central macular thickness (CMT), inner and outer cystoid macular edema (CME) volume, serous retinal detachment (SRD) volume, retinal volume (CME + SRD volume) within the 6-mm Early Treatment of Diabetic Retinopathy Study circle were calculated. OCT biomarkers were followed up throughout treatment. Among total of 144 eyes, 113 patients were included in the bevacizumab only group and 31 patients were included in the switching group. Compared to the bevacizumab only group, the switching group showed higher baseline CMT (558.00 ± 209.60 µm vs. 454.96 ± 125.88 µm, p = 0.003), larger inner CME (6.02 ± 1.43 mm3 vs. 5.12 ± 0.87 mm3, p = 0.004) and SRD volume (0.32 ± 0.40 mm3 vs. 0.11 ± 0.09 mm3, p = 0.015) and higher proportion of patients with SRD (58.06% vs. 31.86%, p = 0.008). In the switching group, CMT, inner CME and SRD volume all showed significant reduction after switching to the dexamethasone implant. DME with large SRD and inner nuclear layer edema volume may be more effectively treated with the dexamethasone implant than bevacizumab.

Multimodal fluorescence lifetime imaging and optical coherence tomography for longitudinal monitoring of tissue-engineered cartilage maturation in a preclinical implantation model.

Cartilage tissue engineering is a promising strategy for effective curative therapies for treatment of osteoarthritis. However, tissue engineers depend predominantly on time-consuming, expensive, and destructive techniques as quality control to monitor the maturation of engineered cartilage. This practice can be impractical for large-scale biomanufacturing and prevents spatial and temporal monitoring of tissue growth, which is critical for the fabrication of clinically relevant-sized cartilage constructs. Nondestructive multimodal imaging techniques combining fluorescence lifetime imaging (FLIm) and optical coherence tomography (OCT) hold great potential to address this challenge. The feasibility of using multimodal FLIm-OCT for nondestructive, spatial, and temporal monitoring of self-assembled cartilage tissue maturation in a preclinical mouse model is investigated. Self-assembled cartilage constructs were developed for 4 weeks in vitro followed by 4 weeks of in vivo maturation in nude mice. Sterile and nondestructive in situ multispectral FLIm and OCT imaging were carried out at multiple time points ( , 4, and 8 weeks) during tissue development. FLIm and 3D volumetric OCT images were reconstructed and used for the analysis of tissue biochemical homogeneity, morphology, and structural integrity. A biochemical homogeneity index was computed to characterize nonhomogeneous tissue growth at different time points. OCT images were validated against histology. FLIm detects heterogenous extracellular matrix (ECM) growth of tissue-engineered cartilage. The outer edge of the tissue construct exhibited longer fluorescence lifetime in 375 to 410 and 450 to 485nm spectral channels, indicating increase in collagen content. Significant ( ) decrease of construct homogeneity index was observed between weeks and weeks. Both FLIm and OCT images revealed defects (voids) at the center of the tissue construct during in vitro culture ( and 4 weeks). Cyst formation during in vivo culture was detected by OCT and confirmed with histology. The ability of multimodal FLIm-OCT to nondestructively monitor the heterogenous growth of engineered tissue constructs in situ is demonstrated. Spatial and temporal variation of construct ECM component was detected by FLIm. OCT reveals structural defects (voids and cysts). This multimodal approach has great potential to replace costly destructive tests in the manufacturing of tissue-engineered medical products, facilitating their clinical translation.

Rapid Cellular-Resolution Skin Imaging with Optical Coherence Tomography Using All-Glass Multifocal Metasurfaces.

Cellular-resolution optical coherence tomography (OCT) is a powerful tool offering noninvasive histology-like imaging. However, like other optical microscopy tools, a high numerical aperture (N.A.) lens is required to generate a tight focus, generating a narrow depth of field, which necessitates dynamic focusing and limiting the imaging speed. To overcome this limitation, we developed a metasurface platform that generates multiple axial foci, which multiplies the volumetric OCT imaging speed by offering several focal planes. This platform offers accurate and flexible control over the number, positions, and intensities of axial foci generated. All-glass metasurface optical elements 8 mm in diameter are fabricated from fused-silica wafers and implemented into our scanning OCT system. With a constant lateral resolution of 1.1 μm over all depths, the multifocal OCT triples the volumetric acquisition speed for dermatological imaging, while still clearly revealing features of stratum corneum, epidermal cells, and dermal-epidermal junctions and offering morphological information as diagnostic criteria for basal cell carcinoma. The imaging speed can be further improved in a sparse sample, e.g., 7-fold with a seven-foci beam. In summary, this work demonstrates the concept of metasurface-based multifocal OCT for rapid virtual biopsy, further providing insights for developing rapid volumetric imaging systems with high resolution and compact volume.

Spatio-temporal optical coherence tomography provides full thickness imaging of the chorioretinal complex.

Despite the rapid development of optical imaging methods, high-resolution invivo imaging with penetration into deeper tissue layers is still a major challenge. Optical coherence tomography (OCT) has been used successfully for non-invasive human retinal volumetric imaging invivo, advancing the detection, diagnosis, and monitoring of various retinal diseases. However, there are important limitations of volumetric OCT imaging, especially coherent noise and the limited axial range over which high resolution images can be acquired. The limited range prevents simultaneous measurement of the retina and choroid with adequate lateral resolution. In this article, we address these limitations with a technique that we term spatio-temporal optical coherence tomography (STOC-T), which uses light with controlled spatial and temporal coherence and advanced signal processing methods. STOC-T enabled the acquisition of high-contrast and high-resolution coronal projection images of the retina and choroid at arbitrary depths.

Volumetric Characterization of Microvasculature in Ex Vivo Human Brain Samples By Serial Sectioning Optical Coherence Tomography.

Serial sectioning optical coherence tomography (OCT) enables accurate volumetric reconstruction of several cubic centimeters of human brain samples. We aimed to identify anatomical features of the ex vivo human brain, such as intraparenchymal blood vessels and axonal fiber bundles, from the OCT data in 3D, using intrinsic optical contrast. We developed an automatic processing pipeline to enable characterization of the intraparenchymal microvascular network in human brain samples. We demonstrated the automatic extraction of the vessels down to a 20 μm in diameter using a filtering strategy followed by a graphing representation and characterization of the geometrical properties of microvascular network in 3D. We also showed the ability to extend this processing strategy to extract axonal fiber bundles from the volumetric OCT image. This method provides a viable tool for quantitative characterization of volumetric microvascular network as well as the axonal bundle properties in normal and pathological tissues of the ex vivo human brain.

Feasibility of Automated Segmentation of Pigmented Choroidal Lesions in OCT Data With Deep Learning.

PurposeTo evaluate the feasibility of automated segmentation of pigmented choroidal lesions (PCLs) in optical coherence tomography (OCT) data and compare the performance of different deep neural networks.MethodsSwept-source OCT image volumes were annotated pixel-wise for PCLs and background. Three deep neural network architectures were applied to the data: the multi-dimensional gated recurrent units (MD-GRU), the V-Net, and the nnU-Net. The nnU-Net was used to compare the performance of two-dimensional (2D) versus three-dimensional (3D) predictions.ResultsA total of 121 OCT volumes were analyzed (100 normal and 21 PCLs). Automated PCL segmentations were successful with all neural networks. The 3D nnU-Net predictions showed the highest recall with a mean of 0.77 ± 0.22 (MD-GRU, 0.60 ± 0.31; V-Net, 0.61 ± 0.25). The 3D nnU-Net predicted PCLs with a Dice coefficient of 0.78 ± 0.13, outperforming MD-GRU (0.62 ± 0.23) and V-Net (0.59 ± 0.24). The smallest distance to the manual annotation was found using 3D nnU-Net with a mean maximum Hausdorff distance of 315 ± 172 µm (MD-GRU, 1542 ± 1169 µm; V-Net, 2408 ± 1060 µm). The 3D nnU-Net showed a superior performance compared with stacked 2D predictions.ConclusionsThe feasibility of automated deep learning segmentation of PCLs was demonstrated in OCT data. The neural network architecture had a relevant impact on PCL predictions.Translational RelevanceThis work serves as proof of concept for segmentations of choroidal pathologies in volumetric OCT data; improvements are conceivable to meet clinical demands for the diagnosis, monitoring, and treatment evaluation of PCLs.

Volumetric subfield analysis of cynomolgus monkey's choroid derived from hybrid machine learning optical coherence tomography segmentation.

This study aimed to provide volumetric choroidal readings regarding sex, origin, and eye side from healthy cynomolgus monkey eyes as a reference database using optical coherence tomography (OCT) imaging. A machine learning (ML) algorithm was used to extract the choroid from the volumetric OCT data. Classical computer vision methods were then applied to automatically identify the deepest location in the foveolar depression. The choroidal thickness was determined from this reference point. A total of 374 eyes of 203 cynomolgus macaques from Asian and Mauritius origin were included in the analysis. The overall subfoveolar mean choroidal volume in zone 1, in the region of the central bouquet, was 0.156 mm3 (range, 0.131-0.193 mm3). For the central choroid volume, the coefficient of variation (CV) was found of 6.3%, indicating relatively little variation. Our results show, based on analyses of variance, that monkey origin (Asian or Mauritius) does not influence choroid volumes. Sex had a significant influence on choroidal volumes in the superior-inferior axis (p ≤ 0.01), but not in the fovea centralis. A homogeneous foveolar choroidal architecture was also observed.

A Diabetic Retinopathy Classification Framework Based on Deep-Learning Analysis of OCT Angiography.

PurposeReliable classification of referable and vision threatening diabetic retinopathy (DR) is essential for patients with diabetes to prevent blindness. Optical coherence tomography (OCT) and its angiography (OCTA) have several advantages over fundus photographs. We evaluated a deep-learning-aided DR classification framework using volumetric OCT and OCTA.MethodsFour hundred fifty-six OCT and OCTA volumes were scanned from eyes of 50 healthy participants and 305 patients with diabetes. Retina specialists labeled the eyes as non-referable (nrDR), referable (rDR), or vision threatening DR (vtDR). Each eye underwent a 3 × 3-mm scan using a commercial 70 kHz spectral-domain OCT system. We developed a DR classification framework and trained it using volumetric OCT and OCTA to classify eyes into rDR and vtDR. For the scans identified as rDR or vtDR, 3D class activation maps were generated to highlight the subregions which were considered important by the framework for DR classification.ResultsFor rDR classification, the framework achieved a 0.96 ± 0.01 area under the receiver operating characteristic curve (AUC) and 0.83 ± 0.04 quadratic-weighted kappa. For vtDR classification, the framework achieved a 0.92 ± 0.02 AUC and 0.73 ± 0.04 quadratic-weighted kappa. In addition, the multiple DR classification (non-rDR, rDR but non-vtDR, or vtDR) achieved a 0.83 ± 0.03 quadratic-weighted kappa.ConclusionsA deep learning framework only based on OCT and OCTA can provide specialist-level DR classification using only a single imaging modality.Translational RelevanceThe proposed framework can be used to develop clinically valuable automated DR diagnosis system because of the specialist-level performance showed in this study.

Visualization of ex vivo rabbit olfactory mucosa and foramina with three-dimensional optical coherence tomography

There is increasing interest in developing a minimally invasive imaging modality to safely evaluate dynamic microscopic changes of the olfactory mucosa and cribriform foramina. Herein, we utilized three-dimensional (3D) optical coherence tomography (OCT) to characterize the ex vivo stratified substructure of olfactory mucosa in rabbits and create 3D reconstructed images of olfactory foramina. Olfactory mucosa and cribriform plates from four New Zealand White rabbits were dissected and imaged using two swept-source OCT systems: (1) 1.3-µm (μm) center wavelength, 100-nm bandwidth, 200-kHz sweep rate, and (2) 1.7-μm center wavelength, 120-nm bandwidth, 90-kHz sweep rate. Volumetric OCT images were compiled to create a 3D reconstruction of the cribriform plate. The ability of OCT to distinguish the olfactory mucosa substructure and foramina was compared to histology. To estimate imaging penetration depth of each system, the first-order exponential decays of depth-resolved intensity were calculated and compared using a paired t-test. Three-dimensional OCT depicted the stratified layered structures within the olfactory mucosa correlating with histology. The epithelium and lamina propria were measured to be 32μm and 107μm in 1.3-μm OCT compared to 30μm and 105μm in histology. Olfactory foramina were visualized via 3D reconstruction. The 1.7-μm system provided greater depth penetration compared to the 1.3-μm system, allowing for improved foramina visualization. We have shown that OCT can be used to image non-pathologic olfactory mucosa and foramina. Implications for this work include diagnostic and therapeutic potentials for neurorhinological and neurodegenerative diseases.

Interplay between traveling wave propagation and amplification at the apex of the mouse cochlea

Sounds entering the mammalian ear produce waves that travel from the base to the apex of the cochlea. An electromechanical active process amplifies traveling wave motions and enables sound processing over a broad range of frequencies and intensities. The cochlear amplifier requires combining the global traveling wave with the local cellular processes that change along the length of the cochlea given the gradual changes in hair cell and supporting cell anatomy and physiology. Thus, we measured basilar membrane (BM) traveling waves in vivo along the apical turn of the mouse cochlea using volumetric optical coherence tomography and vibrometry. We found that there was a gradual reduction in key features of the active process toward the apex. For example, the gain decreased from 23 to 19 dB and tuning sharpness decreased from 2.5 to 1.4. Furthermore, we measured the frequency and intensity dependence of traveling wave properties. The phase velocity was larger than the group velocity, and both quantities gradually decrease from the base to the apex denoting a strong dispersion characteristic near the helicotrema. Moreover, we found that the spatial wavelength along the BM was highly level dependent in vivo, such that increasing the sound intensity from 30 to 90 dB sound pressure level increased the wavelength from 504 to 874 μm, a factor of 1.73. We hypothesize that this wavelength variation with sound intensity gives rise to an increase of the fluid-loaded mass on the BM and tunes its local resonance frequency. Together, these data demonstrate a strong interplay between the traveling wave propagation and amplification along the length of the cochlea.

Higher-order regression three-dimensional motion-compensation method for real-time optical coherence tomography volumetric imaging of the cornea.

.SignificanceOptical coherence tomography (OCT) allows high-resolution volumetric three-dimensional (3D) imaging of biological tissues in vivo. However, 3D-image acquisition can be time-consuming and often suffers from motion artifacts due to involuntary and physiological movements of the tissue, limiting the reproducibility of quantitative measurements.AimTo achieve real-time 3D motion compensation for corneal tissue with high accuracy.ApproachWe propose an OCT system for volumetric imaging of the cornea, capable of compensating both axial and lateral motion with micron-scale accuracy and millisecond-scale time consumption based on higher-order regression. Specifically, the system first scans three reference -mode images along the -axis before acquiring a standard C-mode image. The difference between the reference and volumetric images is compared using a surface-detection algorithm and higher-order polynomials to deduce 3D motion and remove motion-related artifacts.ResultsSystem parameters are optimized, and performance is evaluated using both phantom and corneal (ex vivo) samples. An overall motion-artifact error of microns and processing time of about 3.40 ms for each B-scan was achieved.ConclusionsHigher-order regression achieved effective and real-time compensation of 3D motion artifacts during corneal imaging. The approach can be expanded to 3D imaging of other ocular tissues. Implementing such motion-compensation strategies has the potential to improve the reliability of objective and quantitative information that can be extracted from volumetric OCT measurements.

Choroidal imaging using optical coherence tomography:techniques and interpretations.

The choroid is vascularized membranous tissue that supplies oxygen and nutrients to the photoreceptors and outer retina. Choroidal vessels underlying the retinal pigment epithelium are difficult to visualize by ophthalmoscopy and slit-lamp examinations. Optical coherence tomography (OCT) imaging made significant advancements in the last 2 decades; it allows visualization of the choroid and its vasculature. Enhanced-depth imaging techniques and swept-source OCT provide detailed choroidal images. A recent breakthrough, OCT angiography (OCTA), visualizes blood flow in the choriocapillaris. However, despite using OCTA, it is hard to visualize the choroidal vessel blood flow. In conventional structural OCT the choroidal vessel structure appears as a low-intensity objects. Image-processing techniques help obtain structural information about these vessels. Manual or automated segmentation of the choroid and binarization techniques enable evaluation of choroidal vessels. Viewing the three-dimensional choroidal vasculature is also possible using high-scan speed volumetric OCT. Unfortunately, although choroidal image analyses are possible using the images obtained by commercially available OCT, the built-in function that analyzes the choroidal vasculature may be insufficient to perform quantitative imaging analysis. Physicians must do that themselves. This review summarizes recent choroidal imaging processing techniques and explains the interpretation of the results for the benefit of imaging experts and ophthalmologists alike.