发表机构
University of Minnesota（明尼苏达大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出将扩散MRI微结构映射视为全局反问题，利用未经训练的神经表示和T1加权解剖先验联合重建参数场，在合成和体内数据上优于逐体素及学习方法。
AI 中文摘要
扩散MRI微结构映射（MM）传统上逐体素求解，忽略了组织微结构形成空间有序场这一事实。这种孤立处理使每个估计问题都变得不适定且非凸。我们转而将MM视为一个单一的全局反问题，从受试者的所有测量中联合重建整个参数场。一种未经训练的神经表示提供隐式空间先验并缓解非凸优化，无需训练数据，而共同配准的T1加权解剖结构提供标准协议中免费可用的结构指导。在合成数据和体内数据上，我们的方法与已建立的逐体素和基于学习的方法相比表现更优，表明全局反演是一种有前景的替代方案。
英文摘要
Diffusion MRI microstructure mapping (MM) is conventionally solved voxel by voxel, ignoring the fact that tissue microstructure forms a spatially organized field. This isolation leaves each estimation problem ill-posed and nonconvex. We instead cast MM as a single global inverse problem, reconstructing the entire parameter field jointly from all measurements of a subject. An untrained neural representation supplies implicit spatial priors and eases the nonconvex optimization, requiring no training data, while coregistered T1-weighted anatomy contributes structural guidance that is freely available in standard protocols. On both synthetic and in-vivo data, our method compares favorably with established voxel-wise and learning-based baselines, suggesting global inversion is a promising alternative.
