Abstract
Polarization-sensitive optical coherence tomography (PS-OCT) measures the polarization state of backscattered light from tissues and provides valuable insights into the birefringence properties of biological tissues. Contrastive unpaired translation (CUT) was used in this study to generate a synthetic PS-OCT image from a single OCT image. The challenges related to extensive data requirements relying on labeled datasets using only pixel-wise correlations that make it difficult to efficiently regenerate the periodic patterns observed in PS-OCT images were addressed. The CUT model captures birefringence patterns by leveraging patch-wise correlations from unpaired data, which allows learning of the underlying structural features of biological tissues responsible for birefringence. To demonstrate the performance of the proposed approach, three generative models (Pix2pix, CycleGAN, and CUT) were compared on an in vivo dataset of injured mouse tendons over a six-week healing period. CUT outperformed Pix2pix and CycleGAN by producing high-fidelity synthetic PS-OCT images that closely matched the original PS-OCT images. Pearson correlation and two-way ANOVA tests confirmed the superior performance of CUT (p-value < 0.0001) over the comparison models. Additionally, a ResNet-152 classification model was used to assess tissue damage, which achieved an accuracy of up to 90.13% compared to the original PS-OCT images. This research demonstrates that CUT is superior to conventional methods for generating high-quality synthetic PS-OCT images and offers better improvements in most scenarios, in terms of efficiency and image fidelity.