用物理约束引导扩散模型，加速多参数脑部MRI重建。
Physics informed guided diffusion for accelerated multi-parametric MRI reconstruction
- 结合预训练扩散模型与物理约束作为图像先验
- 重建精度更高，且保持测量一致性与物理模型合规性
- 适合需要高保真多参数映射的医学影像研究者
我们提出MRF-DiPh，一种用于从高度加速的瞬态定量MRI采集（如磁共振指纹图谱）中进行多参数组织映射的新颖物理引导去噪扩散方法。该方法基于近端分裂公式推导，利用预训练的去噪扩散模型作为有效图像先验，正则化MRF逆问题。在重建过程中同时施加两个关键物理约束：(1) k空间测量一致性；(2) 遵循布洛赫响应模型。在活体脑扫描数据上的数值实验表明，MRF-DiPh优于深度学习与压缩感知基线方法，在提供更准确参数图的同时，更好地保持测量保真度与物理模型一致性，这对可靠求解医学影像中的逆问题至关重要。
原文摘要 · Abstract (English)
We introduce MRF-DiPh, a novel physics informed denoising diffusion approach for multiparametric tissue mapping from highly accelerated, transient-state quantitative MRI acquisitions like Magnetic Resonance Fingerprinting (MRF). Our method is derived from a proximal splitting formulation, incorporating a pretrained denoising diffusion model as an effective image prior to regularize the MRF inverse problem. Further, during reconstruction it simultaneously enforces two key physical constraints: (1) k-space measurement consistency and (2) adherence to the Bloch response model. Numerical experiments on in-vivo brain scans data show that MRF-DiPh outperforms deep learning and compressed sensing MRF baselines, providing more accurate parameter maps while better preserving measurement fidelity and physical model consistency-critical for solving reliably inverse problems in medical imaging.
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