通过几何分析提升扩散MRI对脑组织结构的解析力，助力疾病精准诊断。
Geometry of the cumulant series in diffusion MRI
- 基于旋转对称性分解扩散信号，提取不变量以表征组织特性
- 纳入全部峰度不变量后，1189人数据集中的多发性硬化分类准确率提升
- 仅需1-2分钟即可完成全脑扫描，适合临床快速应用
水分子扩散使扩散磁共振成像（dMRI）在微米尺度上对细胞级组织结构敏感。精准医疗与定量成像依赖于揭示dMRI的信息含量，并建立硬件无关的简洁特征指纹。基于旋转群SO(3)对称性，我们研究了dMRI信号的几何结构及其采集拓扑，识别出累积张量的不可约分量与完整不变量集合，并将其与组织性质关联。包含所有峰度不变量可显著提升1189名受试者中多发性硬化病的分类性能。我们设计基于十二面体顶点的最短采集方案，仅用1-2分钟即可确定最常用不变量，实现全脑覆盖。以具有明确对称性的标量不变量图表示dMRI，将为病理、发育与衰老的机器学习分类提供基础，而快速协议则推动先进dMRI向临床转化。
原文摘要 · Abstract (English)
Water diffusion gives rise to micron-scale sensitivity of diffusion MRI (dMRI) to cellular-level tissue structure. Precision medicine and quantitative imaging depend on uncovering the information content of dMRI and establishing its parsimonious hardware-independent fingerprint. Based on the rotational SO(3) symmetry, we study the geometry of the dMRI signal and the topology of its acquisition, identify irreducible components and a full set of invariants for the cumulant tensors, and relate them to tissue properties. Including all kurtosis invariants improves multiple sclerosis classification in a cohort of 1189 subjects. We design the shortest acquisitions based on icosahedral vertices to determine the most used invariants in only 1-2 minutes for whole brain. Representing dMRI via scalar invariant maps with definite symmetries will underpin machine learning classifiers of pathology, development, and aging, while fast protocols will enable translation of advanced dMRI into clinic.
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