Fast and robust standard-deviation-based method for bulk motion compensation in phase-based functional OCT

Wearable optical coherence tomography angiography probe for freely moving mice

Optical coherence tomography (OCT) is an emerging optical imaging technology that holds great potential in medical and biological applications. Apart from its conventional ophthalmic uses, it has found extensive applications in studying various brain activities and disorders in anesthetized/restricted rodents, with a particular focus on visualizing brain blood vessel morphology and function. However, developing a compact wearable OCT probe for studying the brain activity/disorders in freely moving rodents is challenging due to the requirements for stability and lightweight design. Here, we report a robust wearable OCT probe, which, to the best of our knowledge, is the first wearable OCT angiography probe capable of long-term monitoring of mouse brain blood flow. This wearable imaging probe has a maximum scanning speed of 76 kHz, with a 12 µm axial resolution, 5.5 µm lateral resolution, and a large field of view (FOV) of 4 mm × 4 mm. It offers easy assembly and stable imaging, enabling it to capture brain vessels in freely moving rodents. We tested this probe to monitor cerebral hemodynamics for up to 4 hours during the acute ischemic phase after photothrombotic stroke in mice, highlighting the reliability and long-term stability of our probe. This work contributes to the advancement of wearable biomedical imaging.

Artificial intelligence in OCT angiography

Optical coherence tomographic angiography (OCTA) is a non-invasive imaging modality that provides three-dimensional, information-rich vascular images. With numerous studies demonstrating unique capabilities in biomarker quantification, diagnosis, and monitoring, OCTA technology has seen rapid adoption in research and clinical settings. The value of OCTA imaging is significantly enhanced by image analysis tools that provide rapid and accurate quantification of vascular features and pathology. Today, the most powerful image analysis methods are based on artificial intelligence (AI). While AI encompasses a large variety of techniques, machine-learning-based, and especially deep-learning-based, image analysis provides accurate measurements in a variety of contexts, including different diseases and regions of the eye. Here, we discuss the principles of both OCTA and AI that make their combination capable of answering new questions. We also review contemporary applications of AI in OCTA, which include accurate detection of pathologies such as choroidal neovascularization, precise quantification of retinal perfusion, and reliable disease diagnosis.

AI-based monitoring of retinal fluid in disease activity and under therapy

Retinal fluid as the major biomarker in exudative macular disease is accurately visualized by high-resolution three-dimensional optical coherence tomography (OCT), which is used world-wide as a diagnostic gold standard largely replacing clinical examination. Artificial intelligence (AI) with its capability to objectively identify, localize and quantify fluid introduces fully automated tools into OCT imaging for personalized disease management. Deep learning performance has already proven superior to human experts, including physicians and certified readers, in terms of accuracy and speed. Reproducible measurement of retinal fluid relies on precise AI-based segmentation methods that assign a label to each OCT voxel denoting its fluid type such as intraretinal fluid (IRF) and subretinal fluid (SRF) or pigment epithelial detachment (PED) and its location within the central 1-, 3- and 6-mm macular area. Such reliable analysis is most relevant to reflect differences in pathophysiological mechanisms and impacts on retinal function, and the dynamics of fluid resolution during therapy with different regimens and substances. Yet, an in-depth understanding of the mode of action of supervised and unsupervised learning, the functionality of a convolutional neural net (CNN) and various network architectures is needed. Greater insight regarding adequate methods for performance, validation assessment, and device- and scanning-pattern-dependent variations is necessary to empower ophthalmologists to become qualified AI users. Fluid/function correlation can lead to a better definition of valid fluid variables relevant for optimal outcomes on an individual and a population level. AI-based fluid analysis opens the way for precision medicine in real-world practice of the leading retinal diseases of modern times.

Phase-stabilized complex-decorrelation angiography

In this study, we developed a novel phase-stabilized complex-decorrelation (PSCD) optical coherence tomography (OCT) angiography (OCTA) method that can generate high quality OCTA images. This method has been validated using three different types of OCT systems and compared with conventional complex- and amplitude-based OCTA algorithms. Our results suggest that in combination with a pre-processing phase stabilization method, the PSCD method is insensitive to bulk motion phase shifts, less dependent on OCT reflectance than conventional complex methods and demonstrates extended dynamic range of flow signal, in contrast to other two methods.

Artifacts and artifact removal in optical coherence tomographic angiography

Optical coherence tomographic angiography (OCTA) enables rapid imaging of retinal vasculature in three dimensions. While the technique has provided quantification of healthy vessels as well as pathology in several diseases, it is not unusual for OCTA data to contain artifacts that may influence measurement outcomes or defy image interpretation. In this review, we discuss the sources of several OCTA artifacts-including projection, motion, and signal reduction-as well as strategies for their removal. Artifact compensation can improve the accuracy of OCTA measurements, and the most effective use of the technology will incorporate hardware and software that can perform such correction.

Plexus-specific retinal vascular anatomy and pathologies as seen by projection-resolved optical coherence tomographic angiography

Optical coherence tomographic angiography (OCTA) is a novel technology capable of imaging retinal vasculature three-dimensionally at capillary scale without the need to inject any extrinsic dye contrast. However, projection artifacts cause superficial retinal vascular patterns to be duplicated in deeper layers, thus interfering with the clean visualization of some retinal plexuses and vascular pathologies. Projection-resolved OCTA (PR-OCTA) uses post-processing algorithms to reduce projection artifacts. With PR-OCTA, it is now possible to resolve up to 4 distinct retinal vascular plexuses in the living human eye. The technology also allows us to detect and distinguish between various retinal and optic nerve diseases. For example, optic nerve diseases such as glaucoma primarily reduces the capillary density in the superficial vascular complex, which comprises the nerve fiber layer plexus and the ganglion cell layer plexus. Outer retinal diseases such as retinitis pigmentosa primarily reduce the capillary density in the deep vascular complex, which comprises the intermediate capillary plexus and the deep capillary plexus. Retinal vascular diseases such as diabetic retinopathy and vein occlusion affect all plexuses, but with different patterns of capillary loss and vascular malformations. PR-OCTA is also useful in distinguishing various types of choroidal neovascularization and monitoring their response to anti-angiogenic medications. In retinal angiomatous proliferation and macular telangiectasia type 2, PR-OCTA can trace the pathologic vascular extension into deeper layers as the disease progress through stages. Plexus-specific visualization and measurement of retinal vascular changes are improving our ability to diagnose, stage, monitor, and assess treatment response in a wide variety of optic nerve and retinal diseases. These applications will be further enhanced with the continuing improvement of the speed and resolution of the OCT platforms, as well as the development of software algorithms to reduce artifacts, improve image quality, and make quantitative measurements.

Automated diagnosis and segmentation of choroidal neovascularization in OCT angiography using deep learning

Accurate identification and segmentation of choroidal neovascularization (CNV) is essential for the diagnosis and management of exudative age-related macular degeneration (AMD). Projection-resolved optical coherence tomographic angiography (PR-OCTA) enables both cross-sectional and en face visualization of CNV. However, CNV identification and segmentation remains difficult even with PR-OCTA due to the presence of residual artifacts. In this paper, a fully automated CNV diagnosis and segmentation algorithm using convolutional neural networks (CNNs) is described. This study used a clinical dataset, including both scans with and without CNV, and scans of eyes with different pathologies. Furthermore, no scans were excluded due to image quality. In testing, all CNV cases were diagnosed from non-CNV controls with 100% sensitivity and 95% specificity. The mean intersection over union of CNV membrane segmentation was as high as 0.88. By enabling fully automated categorization and segmentation, the proposed algorithm should offer benefits for CNV diagnosis, visualization monitoring.

High dynamic range optical coherence tomography angiography (HDR-OCTA)

The dynamic range of current optical coherence tomography (OCT) angiography (OCTA) images is limited by the fixed scanning intervals. High speed OCT devices introduce the possibility of extending the flow signal dynamic range. In this study, we created a novel scanning pattern for achieving high dynamic range (HDR)-OCTA with a superior scanning efficiency. We implemented a bidirectional, interleaved scanning pattern that is sensitive to different flow speeds by adjustable adjacent inter-scan time intervals. We found that an improved flow dynamic range can be achieved by generating 3 different B-scan time intervals using 3 repetitions.

Differential phase standard-deviation-based optical coherence tomographic angiography for human retinal imaging in vivo

We present a differential phase standard-deviation (DPSD)-based optical coherence tomographic (OCT) angiography (OCTA) technique to calculate the angiography images of the human retina. The standard deviation was calculated along the depth direction on the differential phase image of two B-scans (from the same position, at different times) to contrast dynamic vascular signals. The performance of a DPSD was verified by both phantom and in vivo experiments. When compared to other OCTA algorithms such as phase variance OCT, speckle variance OCT, and optical microangiography, we showed that a DPSD achieved improved image contrast and higher sensitivity. Furthermore, we also found the improved signal-to-noise ratio and contrast-to-noise ratio of 1.6 dB and 0.5, respectively, in large scanning range images.

Automated phase unwrapping in Doppler optical coherence tomography

Phase wrapping is a crucial issue in Doppler optical coherence tomography (OCT) and restricts its automatic implementation for clinical applications that quantify total retinal blood flow. We propose an automated phase-unwrapping technique that takes advantage of the parabolic profile of blood flow velocity in vessels. Instead of inspecting the phase shift manually, the algorithm calculates the gradient magnitude of the phase shift on the cross-sectional image and automatically detects the presence of phase wrapping. The voxels affected by phase wrapping are corrected according to the determined flow direction adjacent to the vessel walls. We validated this technique in the rodent retina using a prototype visible-light OCT and in the human retina with a commercial infrared OCT system. We believe this signal processing method may well accelerate clinical applications of Doppler OCT in ophthalmology.

Maximum value projection produces better en face OCT angiograms than mean value projection

Optical coherence tomography angiography (OCTA) images rely on en face data projections for both qualitative and quantitative interpretation. Both maximum value and mean value projections are commonly used, and many researchers consider them essentially interchangeable approaches. On the contrary, we find that maximum value projection achieves a consistently higher signal-to-noise ratio and higher image contrast across multiple vascular layers, in both healthy eyes and for each disease examined.