FIGURE 1.

Data processing. (A) 2D Θ_Z en face map generation. An example frame from the inner retina angiogram volume is shown, after background correction and elimination of non-inner retinal tissue. The volume is blurred to generate the angiogram mask volume, eigen-decomposed to generate vessel orientation relative to the incident OCT beam, Θ_Z, and Frangi filtered to generate the vesselness volume. Vessel angle Θ_Z (top right) is defined as the angle between OCT incident beam (Z axis) and vessel axis (eigenvector). The Θ_Z volume is thresholded by the angiogram mask volume to remove static tissue. Θ_Z values at maximum vesselness locations (magenta crosses) are projected to create the 2D en face (X and Y) Θ_Z map. A 2D ROI (white polygon), selected from the en face Θ_Z map (A), is then converted to a 3D ROI. (B) Starting from the vesselness frames, the axial positions (magenta crosses) of the maximum vesselness values within the 2D ROI are averaged to estimate the vessel midpoint (gold line). Then, a 7 pixel range (+/− 3 pixels, ~25.8 μm, gold box) centered on this midpoint, is used as the depth (Z) range for the final 3D ROI (the original 2D ROI delimits the 3D ROI in X and Y). Within this 3D ROI, thresholded Θ_Z and angiogram values for each voxel (indexed by i) are averaged and summed, respectively, to yield the vessel orientation (Θ_vessel) and vessel signal (I_vessel). (C) Zoom showing corresponding 2D ROIs on pre- and post-injection en face Θ_Z maps