发表机构
School of Information Engineering, Nanchang University; Academy of Medical Engineering and Translational Medicine, Medical School, Faculty of Medicine, Tianjin University; Research Center for Medical AI, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（南昌大学信息工程学院; 天津大学医学院医学工程与转化医学研究院; 中国科学院深圳先进技术研究院医学人工智能研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出受试者特异性空间-角度高斯场，实现自监督联合k-q dMRI重建，在HCP数据集的多加速设置实验中提升了缺失方向DWI等任务的性能。
AI 中文摘要
扩散MRI需要在多个扩散编码方向上重复采集k空间，因此采集时间取决于空间和角度采样。现有的联合k-q方法要么将方向参数与固定体素关联，要么将空间重建与角度完成分离。然而，不同方向采集的扩散加权图像共享相同的解剖结构，但其局部信号强度随扩散编码而变化。现有公式未充分利用共享解剖结构与方向依赖信号变化之间的互补性，导致残留空间误差可能被误判为真实角度变化并传播到未观测方向。我们提出一种受试者特异性的空间-角度高斯场，用于自监督联合k-q dMRI重建。共享的3D高斯基元提供局部空间支持，每个基元携带连续的q条件张量残差响应。每个位置的信号由多个重叠基元响应合成，耦合相邻空间区域和扩散方向。该场从观测方向的欠采样k空间测量中逐步优化，无需全采样目标或保留方向监督。在三个HCP扩散壳上针对多种加速设置的实验表明，其在缺失方向DWI重建、张量衍生指标及主扩散方向估计方面均取得了一致的改进。
英文摘要
Diffusion MRI requires repeated k-space acquisitions over multiple diffusion-encoding directions, making acquisition time dependent on both spatial and angular sampling. Existing joint k-q methods either associate directional parameters with fixed voxels or separate spatial reconstruction from angular completion. However, diffusion-weighted images acquired under different directions share the same anatomical organization, while their local signal intensities vary with diffusion encoding. Existing formulations do not fully exploit the complementarity between shared anatomy and direction-dependent signal variation. Consequently, residual spatial errors may be misinterpreted as genuine angular variation and propagated to unobserved directions. We propose a subject-specific spatial-angular Gaussian field for self-supervised joint k-q dMRI reconstruction. Shared 3D Gaussian primitives provide local spatial support, with each primitive carrying a continuous q-conditioned tensor-residual response. The signal at each location is synthesized from multiple overlapping primitive responses, coupling neighboring spatial regions and diffusion directions. The field is progressively optimized from undersampled k-space measurements of observed directions, without fully sampled targets or held-out-direction supervision. Experiments on three HCP diffusion shells under multiple acceleration settings demonstrated consistent improvements in missing-direction DWI reconstruction, tensor-derived metrics, and principal diffusion orientation estimation.
Comments11 pages, 8 figures. Preprint submitted to IEEE Journal of Biomedical and Health Informatics
