Diffusion-weighted magnetic resonance imaging (DW-MRI) is a critical imaging method for capturing and modeling tissue microarchitecture at a millimeter scale. A common practice to model the measured DW-MRI signal is via fiber orientation distribution function (fODF). This function is the essential first step for the downstream tractography and connectivity analyses. With recent advantages in data sharing, large-scale multi-site DW-MRI datasets are being made available for multi-site studies. However, measurement variabilities (e.g., inter- and intra-site variability, hardware performance, and sequence design) are inevitable during the acquisition of DW-MRI. Most existing model-based methods (e.g., constrained spherical deconvolution (CSD)) and learning based methods (e.g., deep learning (DL)) do not explicitly consider such variabilities in fODF modeling, which consequently leads to inferior performance on multi-site and/or longitudinal diffusion studies. In this paper, we propose a novel data-driven deep constrained spherical deconvolution method to explicitly constrain the scan-rescan variabilities for a more reproducible and robust estimation of brain microstructure from repeated DW-MRI scans. Specifically, the proposed method introduces a new 3D volumetric scanner-invariant regularization scheme during the fODF estimation. We study the Human Connectome Project (HCP) young adults test-retest group as well as the MASiVar dataset (with inter- and intra-site scan/rescan data). The Baltimore Longitudinal Study of Aging (BLSA) dataset is employed for external validation. From the experimental results, the proposed data-driven framework outperforms the existing benchmarks in repeated fODF estimation. The proposed method is assessing the downstream connectivity analysis and shows increased performance in distinguishing subjects with different biomarkers.

翻译：扩散加权磁共振成像（DW-MRI）是一种关键的成像方法，可在毫米尺度上捕捉和建模组织微观结构。通常，通过纤维取向分布函数（fODF）对测量到的DW-MRI信号进行建模，该函数是下游纤维束追踪和连通性分析的首要基础步骤。随着数据共享的最新进展，大规模多站点DW-MRI数据集正被用于多中心研究。然而，在DW-MRI采集过程中，测量变异性（例如站点内与站点间变异性、硬件性能差异和序列设计差异）不可避免。现有基于模型的方法（如约束球面反卷积（CSD））和基于学习的方法（如深度学习（DL））在fODF建模中均未显式考虑此类变异性，这导致它们在多站点和/或纵向扩散研究中性能不佳。本文提出一种新颖的数据驱动深度约束球面反卷积方法，通过显式约束扫描-重扫描变异性，实现从重复DW-MRI扫描中对脑微观结构进行更可重复且鲁棒的估计。具体而言，所提出的方法在fODF估计过程中引入了一种新型三维体素扫描仪不变正则化方案。我们利用人类连接组计划（HCP）青年测试-重测组以及MASiVar数据集（包含站点内和站点间扫描/重扫描数据）进行研究，并采用巴尔的摩纵向衰老研究（BLSA）数据集进行外部验证。实验结果表明，所提出的数据驱动框架在重复fODF估计中优于现有基准方法。该方法在下游连通性分析中得到评估，并在区分携带不同生物标志物的受试者方面展现出更优性能。