Unsupervised Head PD-to-T2 MR Image Translation via Multi-Scale Feature Regularization

Unsupervised medical image translation remains challenging because model development often relies on unpaired training, whereas reliable evaluation requires well-matched reference images. PD-weighted and T2-weighted brain MR images provide a useful testbed for this problem because they are closely matched anatomically while still exhibiting distinct contrast characteristics. Existing methods often align only high-level features, overlooking low-level texture details that are important for structural fidelity. In this work, we propose the Multi-Scale Feature Regularization and Patch Mixup (MSFRPM) framework based on an encoder–decoder architecture. It aligns cross-domain features across multiple scales to preserve local details and employs a patch-based mixup strategy to augment training data. The framework was evaluated using an unsupervised learning protocol with strict data partitioning. Experimental results demonstrate that MSFRPM achieves strong performance relative to eight state-of-the-art methods. Our approach achieved improvements in MAE (6.26 ± 0.86), PSNR (23.53 ± 0.92), SSIM (0.83 ± 0.03), and GMSD (0.100 ± 0.010). Qualitative assessments confirmed improved structural fidelity, and t-SNE visualization validated enhanced cross-domain feature alignment. Overall, MSFRPM provides a useful approach for unsupervised PD-to-T2 image translation under the current experimental setting.