[2603.03769] IntroductionDMD-augmented Unpaired Neural Schrödinger Bridge for Ultra-Low Field MRI Enhancement

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.03769 (cs) [Submitted on 4 Mar 2026] Title:IntroductionDMD-augmented Unpaired Neural Schrödinger Bridge for Ultra-Low Field MRI Enhancement Authors:Youngmin Kim, Jaeyun Shin, Jeongchan Kim, Taehoon Lee, Jaemin Kim, Peter Hsu, Jelle Veraart, Jong Chul Ye View a PDF of the paper titled IntroductionDMD-augmented Unpaired Neural Schr\"odinger Bridge for Ultra-Low Field MRI Enhancement, by Youngmin Kim and 7 other authors View PDF HTML (experimental) Abstract:Ultra Low Field (64 mT) brain MRI improves accessibility but suffers from reduced image quality compared to 3 T. As paired 64 mT - 3 T scans are scarce, we propose an unpaired 64 mT $\rightarrow$ 3 T translation framework that enhances realism while preserving anatomy. Our method builds upon the Unpaired Neural Schrödinge Bridge (UNSB) with multi-step refinement. To strengthen target distribution alignment, we augment the adversarial objective with DMD2-style diffusion-guided distribution matching using a frozen 3T diffusion teacher. To explicitly constrain global structure beyond patch-level correspondence, we combine PatchNCE with an Anatomical Structure Preservation (ASP) regularizer that enforces soft foreground background consistency and boundary aware constraints. Evaluated on two disjoint cohorts, the proposed framework achieves an improved realism structure trade-off, enhancing distribution level realism on unpaired benchmarks while increasing st...