Jones matrix-based speckle-decorrelation angiography using polarization-sensitive optical coherence tomography

Deep learning-based label-free imaging of lymphatics and aqueous veins in the eye using optical coherence tomography

We demonstrate an adaptation of deep learning for label-free imaging of the micro-scale lymphatic vessels and aqueous veins in the eye using optical coherence tomography (OCT). The proposed deep learning-based OCT lymphangiography (DL-OCTL) method was trained, validated and tested, using OCT scans (23 volumetric scans comprising 19,736 B-scans) from 11 fresh ex vivo porcine eyes with the corresponding vessel labels generated by a conventional OCT lymphangiography (OCTL) method based on thresholding with attenuation compensation. Compared to conventional OCTL, the DL-OCTL method demonstrates comparable results for imaging lymphatics and aqueous veins in the eye, with an Intersection over Union value of 0.79 ± 0.071 (mean ± standard deviation). In addition, DL-OCTL mitigates the imaging artifacts in conventional OCTL where the OCT signal modelling was corrupted by the tissue heterogeneity, provides ~ 10 times faster processing based on a rough comparison and does not require OCT-related knowledge for correct implementation as in conventional OCTL. With these favorable features, DL-OCTL promises to improve the practicality of OCTL for label-free imaging of lymphatics and aqueous veins for preclinical and clinical imaging applications.

Feasibility of deep learning-based polarization-sensitive optical coherence tomography angiography for imaging cutaneous microvasculature

Polarization-sensitive optical coherence tomography (PS-OCT) measures the polarization states of the backscattered light from tissue that can improve angiography based on conventional optical coherence tomography (OCT). We present a feasibility study on PS-OCT integrated with deep learning for PS-OCT angiography (PS-OCTA) imaging of human cutaneous microvasculature. Two neural networks were assessed for PS-OCTA, including the residual dense network (RDN), which previously showed superior performance for angiography with conventional OCT and the upgraded grouped RDN (GRDN). We also investigated different protocols to process the multiple signal channels provided by the Jones matrices from the PS-OCT system to achieve optimal PS-OCTA performance. The training and testing of the deep learning-based PS-OCTA were performed using PS-OCT scans collected from 18 skin locations comprising 16,600 B-scan pairs. The results demonstrated a moderately improved performance of GRDN over RDN, and of the use of the combined signal from the Jones matrix elements over the separate use of the elements, as well as a similar image quality to that provided by speckle decorrelation angiography. GRDN-based PS-OCTA also showed ∼2-3 times faster processing and improved mitigation of tissue motion as compared to speckle decorrelation angiography, and enabled fully automatic processing. Deep learning-based PS-OCTA can be used for imaging cutaneous microvasculature, which may enable easy adoption of PS-OCTA for preclinical and clinical applications.

Comparative study of OCTA algorithms with a high-sensitivity multi-contrast Jones matrix OCT system for human skin imaging

The multi-contrast Jones matrix OCT (JMT) system can not only improve the tissue-specific contrast but also enhance the sensitivity of detecting flow, which is gaining increasing attention. However, for the JMT system, there is currently a lack of studies evaluating and guiding the selection of suitable angiography algorithms to map the most appealing quality of angiograms for clinical use. In this paper, by a homemade high-sensitivity multi-contrast JMT system based 200 kHz swept source, the performance of two complex-signal-based OCTA methods that are insensitive to phase instability and one amplitude-signal-based OCTA method are compared for in-vivo imaging of human skin qualitatively and quantitatively. Six metrics, including vascular connectivity, image contrast-to-noise ratio, image signal-to-noise ratio, vessel diameter index, blood vessel density, and processing time, are assessed. The results show that the vascular networks processed by all OCTA methods and the texture of skin could be visualized simultaneously and markedly. Additionally, the complex-signal-based OCTA methods successfully suppress phase instabilities and even outperform the amplitude-signal-based OCTA algorithm in some indicators. This paper has a certain guiding significance for selecting an appropriate angiography algorithm and expanding the application field with this system.

The Development and Clinical Application of Innovative Optical Ophthalmic Imaging Techniques

The field of ophthalmic imaging has grown substantially over the last years. Massive improvements in image processing and computer hardware have allowed the emergence of multiple imaging techniques of the eye that can transform patient care. The purpose of this review is to describe the most recent advances in eye imaging and explain how new technologies and imaging methods can be utilized in a clinical setting. The introduction of optical coherence tomography (OCT) was a revolution in eye imaging and has since become the standard of care for a plethora of conditions. Its most recent iterations, OCT angiography, and visible light OCT, as well as imaging modalities, such as fluorescent lifetime imaging ophthalmoscopy, would allow a more thorough evaluation of patients and provide additional information on disease processes. Toward that goal, the application of adaptive optics (AO) and full-field scanning to a variety of eye imaging techniques has further allowed the histologic study of single cells in the retina and anterior segment. Toward the goal of remote eye care and more accessible eye imaging, methods such as handheld OCT devices and imaging through smartphones, have emerged. Finally, incorporating artificial intelligence (AI) in eye images has the potential to become a new milestone for eye imaging while also contributing in social aspects of eye care.

OCTAVA: An open-source toolbox for quantitative analysis of optical coherence tomography angiography images

Optical coherence tomography angiography (OCTA) performs non-invasive visualization and characterization of microvasculature in research and clinical applications mainly in ophthalmology and dermatology. A wide variety of instruments, imaging protocols, processing methods and metrics have been used to describe the microvasculature, such that comparing different study outcomes is currently not feasible. With the goal of contributing to standardization of OCTA data analysis, we report a user-friendly, open-source toolbox, OCTAVA (OCTA Vascular Analyzer), to automate the pre-processing, segmentation, and quantitative analysis of en face OCTA maximum intensity projection images in a standardized workflow. We present each analysis step, including optimization of filtering and choice of segmentation algorithm, and definition of metrics. We perform quantitative analysis of OCTA images from different commercial and non-commercial instruments and samples and show OCTAVA can accurately and reproducibly determine metrics for characterization of microvasculature. Wide adoption could enable studies and aggregation of data on a scale sufficient to develop reliable microvascular biomarkers for early detection, and to guide treatment, of microvascular disease.

Model-based optical coherence tomography angiography enables motion-insensitive vascular imaging

We present a significant step toward ultrahigh-resolution, motion-insensitive characterization of vascular dynamics. Optical coherence tomography angiography (OCTA) is an invaluable diagnostic technology for non-invasive, label-free vascular imaging in vivo. However, since it relies on detecting moving cells from consecutive scans, high-resolution OCTA is susceptible to tissue motion, which imposes challenges in resolving and quantifying small vessels. We developed a novel OCTA technique named ultrahigh-resolution factor angiography (URFA) by modeling repeated scans as generative latent variables, with a common variance representing shared features and a unique variance representing motion. By iteratively maximizing the combined log-likelihood probability of these variances, the unique variance is largely separated. Meanwhile, features in the common variance are decoupled, in which vessels with dynamic flow are extracted from tissue structure by integrating high-order factors. Combined with Gabor-domain optical coherence microscopy, URFA successfully extracted high-resolution cutaneous vasculature despite severe involuntary tissue motion and scanner oscillation, significantly improving the visualization and characterization of micro-capillaries in vivo. Compared with the conventional approach, URFA reduces motion artifacts by nearly 50% on average, evaluated on local differences.

Advances in multimodal imaging in ophthalmology

Multimodality ophthalmic imaging systems aim to enhance the contrast, resolution, and functionality of existing technologies to improve disease diagnostics and therapeutic guidance. These systems include advanced acquisition and post-processing methods using optical coherence tomography (OCT), combined scanning laser ophthalmoscopy and OCT systems, adaptive optics, surgical guidance, and photoacoustic technologies. Here, we provide an overview of these ophthalmic imaging systems and their clinical and basic science applications.

Polarization sensitive optical coherence tomography for imaging microvascular information within living tissue without polarization-induced artifacts

When imaging birefringent samples using optical coherence tomography angiography (OCTA), the phase retardation may appear opposite to the phase change due to the blood flow in the orthogonal signals, for which a cancellation effect can occur when deriving OCTA signals. This effect can diminish the ability of OCTA to detect vascular information, leading to an erroneous interpretation of the final OCTA images. To mitigate this issue, we demonstrate polarization-sensitive optical coherence tomography (PS-OCT) to image microvascular information within a living sample without polarization induced artifacts. The system is furnished with a swept source OCT (SS-OCT) that incorporates two imaging modes: OCTA imaging and polarization-sensitive imaging. PS-OCT is used to provide birefringent contrast where the color-encoded Stokes parameters are used to obtain high contrast polarization-state images. OCTA is used to acquire high-resolution images of functional microvascular networks permeating the scanned tissue volume. Taking the advantages of the dual-channel PS-OCT configuration, the polarization induced artifacts are eliminated from OCTA vascular imaging. The proposed PS-OCTA system is employed to visualize the birefringent components and the vascular networks of the human skin in vivo. It is expected that the proposed system setup would have useful and practical applications in the investigations of the vasculature in the birefringent tissue samples both pre-clinically and clinically.