DMD-augmented Unpaired Neural Schr"odinger Bridge for Ultra-Low Field MRI Enhancement
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Abstract (arXiv)
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 Schr"odinge 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 structural fidelity on the paired cohort compared to unpaired baselines.
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References · 9
- 2023One-step Diffusion with Distribution Matching Distillationcited by 1,019
- 2024Improved Distribution Matching Distillation for Fast Image Synthesiscited by 684
- 2020Contrastive Learning for Unpaired Image-to-Image Translationcited by 639
- 2018Distribution Matching Losses Can Hallucinate Features in Medical Image Translationcited by 99
- 2023Unpaired Image-to-Image Translation via Neural Schr"odinger Bridgecited by 96
- 2021Breaking the Dilemma of Medical Image-to-image Translationcited by 82
- 2023Feature Extraction for Generative Medical Imaging Evaluation: New Evidence Against an Evolving Trendcited by 24
- 2024Demystifying μcited by 7
- 2024Anatomical Conditioning for Contrastive Unpaired Image-to-Image Translation of Optical Coherence Tomography Imagescited by 2
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