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.

本文提出了一种新颖的数据驱动的深度约束球形反卷积方法，用于对重复 DW-MRI 扫描的脑微结构进行更可重复和更健壮的估计，并表明该方法在区分具有不同生物标志物的受试者方面具有更好的表现。