Signal-aware synthesis of tissue polarization uniformity from OCT images guided by an SNR-based heuristic

• A signal-aware generative model is proposed for polarization image synthesis from optical coherence tomography. • Physics-guided attention is designed based on the nonlinear relation between polarization uniformity and signal-to-noise ratio. • The model effectively preserves structural and polarization-specific features. • Strong generalization is demonstrated across distinct tissue types without retraining. • The framework is lightweight and computationally efficient for practical deployment. Polarization-sensitive optical coherence tomography (PS-OCT) is a powerful imaging modality that captures both structural and polarization-related tissue features, offering significant diagnostic value. Among these, the degree of polarization uniformity (DOPU) is critical for characterizing tissue microstructure. However, obtaining DOPU images typically requires specialized hardware and complex system configurations. To address this limitation, we propose a knowledge-guided deep generative framework, signal attention GAN (SA-GAN), to synthesize DOPU images directly from standard OCT intensity scans. SA-GAN integrates a signal-guided attention mechanism inspired by signal-to-noise ratio (SNR) principles, enabling selective focus on regions with meaningful polarization patterns while suppressing noise-dominated areas. This design allows for the generation of accurate, high-fidelity DOPU images without additional imaging hardware. We validated SA-GAN on three independent datasets: SKIN-PSOCT, CARTILAGE-PSOCT, and the public Retinal-OCT2017 dataset. On SKIN-PSOCT, SA-GAN achieved a structural similarity index measure (SSIM) of 97.8% and a peak signal-to-noise ratio (PSNR) of 28.6 dB. On CARTILAGE-PSOCT, it reached an SSIM of 93.9% and a PSNR of 24.8 dB in cross-dataset testing. Applied to the Retinal-OCT2017 dataset, SA-GAN achieved state-of-the-art performance in a four-class retinal disease classification task. These results demonstrate the robustness and generalizability of our method. SA-GAN provides a cost-effective and practical solution to extend PS-OCT capabilities, supporting the development of intelligent imaging systems for biomedical diagnostics and digital medicine. Our code is available via this link: https://github.com/Yuhengw/SA-GAN .