用扩散模型提升64mT脑MRI清晰度，无需配对数据
DMD-augmented Unpaired Neural Schrödinger Bridge for Ultra-Low Field MRI Enhancement
- 基于无配对的量子桥接框架，引入扩散教师指导分布对齐
- 在无配对数据上实现更高真实感，配对数据上结构保真度提升
- 适合低场MRI重建、医学图像生成方向的研究者
64 mT超低场脑MRI虽提升可及性，但图像质量远低于3 T。由于配对的64 mT与3 T扫描数据稀缺，本文提出一种无配对的64 mT → 3 T图像转换框架，兼顾真实感与解剖结构保留。方法在无配对神经薛定谔桥（UNSB）基础上引入多步精炼机制，通过冻结的3T扩散教师模型，以DMD2风格的扩散引导分布匹配增强目标分布对齐；同时结合PatchNCE与解剖结构保真（ASP）正则化，强制前景背景一致性及边界感知约束，以显式约束全局结构。在两个独立队列上评估，该框架在无配对基准上提升分布级真实感，在配对队列上显著提高结构保真度，优于现有无配对基线。
原文摘要 · Abstract (English)
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 structural fidelity on the paired cohort compared to unpaired baselines.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。