We proposed a neural network to generate volumetric dynamic optical coherence tomography (DOCT) from small-number OCT frames. In this study, we used a DOCT method (i.e., logarithmic OCT intensity variance; LIV) and it is applied to tumor spheroid samples. A U-Net-based NN model was trained to generate a LIV image from only 4 OCT frames. The NN-generated LIV was subjectively and objectively compared with conventional LIV images generated from 32 frames. The comparison showed a high similarity between the NN-generated LIV and the conventional LIV. This NN-based method enabled volumetric DOCT with only 6.55 s acquisition time.
Degree of polarization uniformity (DOPU) provides promising biomarkers of abnormalities in the retinal pigment epithelium (RPE) and is obtained by polarization-sensitive optical coherence tomography (OCT), which is not commercially available. A U-Net shape model was used to synthesize retinal DOPU from OCT with OCT angiography (OCTA) images. Sets of OCT, OCTA, and DOPU images from 175 subjects, 107 subjects, and 30 subjects were used for training, validation, and evaluation, respectively. RPE abnormalities were compared between True DOPU and synthesized DOPU. Healthy structure, RPE elevation, and RPE thickening were synthesized with high recall and precision. However, further improvements are required for RPE defect and hyperreflective retina foci synthesizing.
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