标题： 基于物理信息的引导扩散加速多参数磁共振成像重建 显示英文标题

标题： Physics informed guided diffusion for accelerated multi-parametric MRI reconstruction

摘要： 我们引入了MRF-DiPh，一种新颖的物理信息去噪扩散方法，用于从高度加速的瞬态定量MRI采集（如磁共振指纹成像（MRF））中进行多参数组织映射。 我们的方法来源于一种近端分裂公式，结合了一个预训练的去噪扩散模型作为有效的图像先验，以正则化MRF逆问题。 此外，在重建过程中，它同时强制执行两个关键的物理约束：（1）k空间测量一致性以及（2）遵循Bloch响应模型。 在活体脑扫描数据上的数值实验表明，MRF-DiPh优于深度学习和压缩感知MRF基线方法，在更好地保持测量保真度和物理模型一致性的同时，提供了更准确的参数图，这对于在医学成像中可靠地解决逆问题至关重要。 显示英文摘要

摘要： 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.