提出可处理脑部扩散磁共振数据对称性的新网络，提升纤维追踪精度。
Equivariant spatio-hemispherical networks for diffusion MRI deconvolution
- 设计对称性保持的卷积层，适应空间与方向变化
- 在模拟和真实数据中显著提升交叉纤维解析能力
- 适合神经影像分析与脑连接图谱研究者
扩散磁共振（dMRI）图像中每个体素包含一个球面信号，反映脑内水分子扩散的方向与强度。本文通过构建对 $ extbf{E(3) imes SO(3)}$ 群等变的卷积层，实现对空间平移、旋转、反射及体素内方向旋转的物理对称性建模。同时，利用神经纤维通常具有反向对称性的特性，设计高效的球半球图卷积，加速高维dMRI数据的分析。在稀疏球面纤维去卷积任务中，所提方法显著提升白质微结构恢复的性能与效率，改善交叉纤维分辨与纤维束追踪效果。实验结果在仿真数据与人体在体数据上均具一致性。
原文摘要 · Abstract (English)
Each voxel in a diffusion MRI (dMRI) image contains a spherical signal corresponding to the direction and strength of water diffusion in the brain. This paper advances the analysis of such spatio-spherical data by developing convolutional network layers that are equivariant to the $\mathbf{E(3) \times SO(3)}$ group and account for the physical symmetries of dMRI including rotations, translations, and reflections of space alongside voxel-wise rotations. Further, neuronal fibers are typically antipodally symmetric, a fact we leverage to construct highly efficient spatio-hemispherical graph convolutions to accelerate the analysis of high-dimensional dMRI data. In the context of sparse spherical fiber deconvolution to recover white matter microstructure, our proposed equivariant network layers yield substantial performance and efficiency gains, leading to better and more practical resolution of crossing neuronal fibers and fiber tractography. These gains are experimentally consistent across both simulation and in vivo human datasets.
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