Abstract: Disclosed are methods and systems for adaptive optics (AO)-optical coherence tomography (OCT) and OCT angiography (OCTA). Embodiments include techniques to generate one or more volumetric and/or depth-resolved figures of merit to guide optimization of ocular aberrations in sensorless AO-OCT and/or AO-OCTA. The one or more figures of merit may be generated in real-time, e.g., in parallel with the OCT scan and/or aberration optimization process. Other embodiments may be described and claimed.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.