Oregon Health & Science University

Other

Yali Jia

David E Huang

Jie Dong Wang

Ou Tan

Jason Tokayer

Yukun Guo

Miao Zhang

Gangjun Liu

Acner Camino

Xiang Wei

Yan Li

Simon Gao

Miao Zhong

Li Liu

Shaohua Pi

David Huang

Disclosed are systems and methods for generating wide-field optical coherence tomography angiography (OCTA) images. In embodiments, multiple OCTA scans of a sample are automatically acquired at overlapping locations. The systems and methods include functionality to adaptively control the scanning procedure such that eye blink and eye motion events are detected in real time and accounted for during 3D scan acquisition. Also disclosed are methods for detecting and correcting motion-related artifacts in OCTA datasets which allow for the longer scan times over larger fields of view required for wide-field imaging. These methods may include division of en face angiogram images into a set of motion-free parallel strips, and application of gross and fine registration methods to align overlapping strips into a motion-corrected composite image. A series of overlapping motion-corrected composite images may be combined into a larger montage to enable wide-field OCTA imaging using multiple OCTA scans.

Systems and methods for automated widefield optical coherence tomography angiography

Disclosed herein are methods and systems for capillary oximetry (e.g., retinal capillary oximetry) using optical coherence tomography (OCT). The method may include obtaining an OCT angiography dataset, performing capillary segmentation based on the OCT angiography dataset to obtain capillary segments, resampling, registering, and/or averaging B-scans of the OCT angiography dataset that correspond to a first capillary segment of the capillary segments to obtain an averaged B-scan for the first capillary segment, determining an anterior and posterior border of the first capillary segment, and determining an oxygen saturation of the first capillary segment based on the averaged B-scan, the anterior border, and the posterior border. Other embodiments may be described and claimed.

Systems and methods for capillary oximetry using optical coherence tomography

This disclosure describes systems, devices, and techniques for training neural networks to identify avascular and signal reduction areas of Optical Coherence Tomography Angiography (OCTA) images and for using trained neural networks. By identifying signal reduction areas in OCTA images, the avascular areas can be detected with high accuracy, even when the OCTA images include artifacts and other types of noise. Accordingly, various implementations described herein can accurately identify avascular areas from real-world clinical OCTA images. In various implementations, a method can include identifying images of retinas. The images may include thickness images, reflectance intensity maps, and OCTA images of the retinas. Avascular maps corresponding to the OCTA images can be identified. A neural network can be trained based on the images and the avascular maps.

Detecting avascular and signal reduction areas in retinas using neural networks

Disclosed are methods and systems correcting bulk-motion artifacts in phase-based functional OCT images. The disclosed methods and systems are based on the use of the standard deviation of the phase shift signal present in phase-based OCT imaging. When applied with functional OCT techniques such as OCT angiography, Doppler OCT, and OCT elastography, the disclosed methods provide improved image quality and decreased computational cost compared to other methods of bulk motion compensation.

Systems and methods for bulk motion compensation in phase-based functional optical coherence tomograpgy

Disclosed herein are methods and systems for optical coherence tomography (OCT) angiography (OCTA). An interleaved scanning pattern is described herein for both raster and bidirectional scanning methods. The interleaved scanning pattern provides B-scans with different scanning intervals. OCTA images based on the B-scans may be combined to obtain a high dynamic range (HDR) OCTA image. Other embodiments may be described and claimed.

Systems and methods for high dynamic range optical coherence tomography angiography (HDR-OCTA)

Methods and systems for identifying CNV membranes and vasculature in images obtained using noninvasive imaging techniques are described. An example method includes generating, by a first model based on at least one image of a retina of a subject, a membrane mask indicating a location of a CNV membrane in the retina. The method further includes generating, by a second model based on the membrane mask and the at least one image, a vasculature mask of the retina of the subject, the vasculature mask indicating CNV vascularization in the retina.

Noninvasive techniques for identifying choroidal neovascularization in retinal scans

Methods for automatically identifying retinal boundaries from a reflectance image are disclosed. An example of the method includes identifying a reflectance image of the retina of a subject; generating a gradient map of the reflectance image, the gradient map representing dark-to-light or light-to-dark reflectance differentials between adjacent pixel pairs in the reflectance image; generating a guidance point array corresponding to a retinal layer boundary depicted in the reflectance image using the gradient map; generating multiple candidate paths estimating the retinal layer boundary in the reflectance image by performing a guided bidirectional graph search on the reflectance image using the guidance point array; and identifying the retinal layer boundary by merging two or more of the multiple candidate paths.

Identifying retinal layer boundaries

Described herein is an optical coherence tomograph (OCT) angiography technique based on the comparison of OCT signal amplitude to provide flow information. The full OCT spectrum can be split into several narrower spectral bands, resulting in the OCT resolution cell in each band being isotropic and less susceptible to axial motion nose. Inter-B-scan flow values can be determined using the individual spectral bands separately and then averaged. Recombining the flow images from the spectral bands yields angiograms that use the full information in the entire OCT spectral range. Such images provide significant improvement of signal-to-noise ratio (SNR) for both flow detection and connectivity of microvascular networks compared to other techniques. Further, creation of isotropic resolution cells can be useful for quantifying flow having equal sensitivity to axial and transverse flow.

In vivo optical flow imaging

Disclosed herein are methods and systems for automated detection of shadow artifacts in optical coherence tomography (OCT) and/or OCT angiography (OCTA). The shadow detection includes applying a machine-learning algorithm to the OCT dataset and the OCTA dataset to detect one or more shadow artifacts in the sample. The machine-learning algorithm is trained with first training data from first training samples that include manufactured shadows and no perfusion defects and second training data from second training samples that include perfusion defects and no manufactured shadows. The shadow artifacts in the OCTA dataset and/or OCT dataset may be suppressed to generate a shadow-suppressed OCTA dataset and/or a shadow-suppressed OCT dataset, respectively. Other embodiments may be described and claimed.

Automated detection of shadow artifacts in optical coherence tomography angiography

An example method includes generating, using a multi-scale block of a convolutional neural network (CNN), a first output image based on an optical coherence tomography (OCT) reflectance image of a retina and an OCT angiography (OCTA) image of the retina. The method further includes generating, using an encoder of the CNN, at least one second output image based on the first output image and generating, using a decoder of the CNN, a third output image based on the at least one second output image. An avascular map is generated based on the third output image. The avascular map indicates at least one avascular area of the retina depicted in the OCTA image.

Detecting avascular areas using neural networks

Methods of applying OCT angiography are disclosed. In particular, methods of detecting, visualizing and measuring the extent of retinal neovascularization are disclosed. Further disclosed are methods measuring retinal nonperfusion area and choriocapillaris defect area.

Optical coherence tomography angiography methods

Embodiments provide systems and methods associated with a reflectance-based projection-resolved (rbPR) optical coherence tomography angiography (OCTA) algorithm which uses optical coherence tomography (OCT) reflectance to enhance the flow signal and suppress the projection artifacts in 3-dimensional OCTA. rbPR improves the vascular connectivity and improved the discrimination of the deeper plexus angiograms in healthy eyes, compared to prior PR-OCTA method. Additionally, rbPR removes flow projection artifacts more completely from the outer retinal slab in the eyes with age-related macular degeneration, and preserves vascular integrity of the intermediate and deep capillary plexuses in the eyes with diabetic retinopathy. Additionally, the rbPR method improves the resolution of the choriocapillaris and demonstrates details comparable to scanning electron microscopy.

Systems and methods for reflectance-based projection-resolved optical coherence tomography angiography

Methods and systems for suppressing shadowgraphic flow projection artifacts in OCT angiography images of a sample are disclosed. In one example approach, normalized OCT angiography data is analyzed at the level of individual A-scans to classify signals as either flow or projection artifact. This classification information is then used to suppress projection artifacts in the three dimensional OCT angiography dataset.

Systems and methods to remove shadowgraphic flow projections on OCT angiography

Described herein is an algorithm to remove decorrelation noise due to bulk motion in optical coherence tomography angiography (OCTA). OCTA B-frames are divided into segments within which the bulk motion velocity could be assumed constant. This velocity is recovered using linear regression of decorrelation versus the logarithm of reflectance in axial lines (A-lines) identified as bulk tissue by percentile analysis. The fitting parameters are used to calculate a reflectance-adjusted threshold for bulk motion decorrelation. Below this threshold, voxels are identified as non-flow tissue, and their flow values are set to zeros. Above this threshold, the voxels are identified as flow voxels and bulk motion velocity is subtracted from each using a nonlinear decorrelation-velocity relationship. Compared to the median-subtraction method, the bulk motion subtraction algorithm described herein improves angiogram signal-to-noise ratio, contrast, vessel density repeatability, and bulk motion noise cleanup, while preserving the connectivity of the vascular networks in the angiogram.

Growing community of inventors

Portland, OR, United States of America

Yali Jia