Elaldi Axel, Dey Neel, Kim Heejong, Gerig Guido
Department of Computer Science and Engineering, New York University, New York, USA.
Inf Process Med Imaging. 2021 Jun;12729:267-278. doi: 10.1007/978-3-030-78191-0_21. Epub 2021 Jun 14.
We present a rotation-equivariant self-supervised learning framework for the sparse deconvolution of non-negative scalar fields on the unit sphere. Spherical signals with multiple peaks naturally arise in Diffusion MRI (dMRI), where each voxel consists of one or more signal sources corresponding to anisotropic tissue structure such as white matter. Due to spatial and spectral partial voluming, clinically-feasible dMRI struggles to resolve crossing-fiber white matter configurations, leading to extensive development in spherical deconvolution methodology to recover underlying fiber directions. However, these methods are typically linear and struggle with small crossing-angles and partial volume fraction estimation. In this work, we improve on current methodologies by nonlinearly estimating fiber structures via self-supervised spherical convolutional networks with guaranteed equivariance to spherical rotation. We perform validation via extensive single and multi-shell synthetic benchmarks demonstrating competitive performance against common base-lines. We further show improved downstream performance on fiber tractography measures on the Tractometer benchmark dataset. Finally, we show downstream improvements in terms of tractography and partial volume estimation on a multi-shell dataset of human subjects.
我们提出了一种旋转等变自监督学习框架，用于单位球面上非负标量场的稀疏反卷积。具有多个峰值的球面信号自然出现在扩散磁共振成像（dMRI）中，其中每个体素由一个或多个对应于各向异性组织结构（如白质）的信号源组成。由于空间和频谱部分容积效应，临床上可行的dMRI难以解析交叉纤维白质构型，从而促使球面反卷积方法得到广泛发展，以恢复潜在的纤维方向。然而，这些方法通常是线性的，并且在处理小交叉角和部分体积分数估计时存在困难。在这项工作中，我们通过具有保证的球面旋转等变性的自监督球面卷积网络非线性估计纤维结构，从而改进了当前的方法。我们通过广泛的单壳和多壳合成基准测试进行验证，展示了与常见基线相比具有竞争力的性能。我们进一步在Tractometer基准数据集上的纤维束成像测量中展示了改进的下游性能。最后，我们在人类受试者的多壳数据集上展示了在纤维束成像和部分体积估计方面的下游改进。
