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Diffusion-weighted magnetic resonance imaging (DW-MRI) is a critical imagingmethod for capturing and modeling tissue microarchitecture at a millimeterscale. A common practice to model the measured DW-MRI signal is via fiberorientation distribution function (fODF). This function is the essential firststep for the downstream tractography and connectivity analyses. With recentadvantages in data sharing, large-scale multi-site DW-MRI datasets are beingmade available for multi-site studies. However, measurement variabilities(e.g., inter- and intra-site variability, hardware performance, and sequencedesign) are inevitable during the acquisition of DW-MRI. Most existingmodel-based methods (e.g., constrained spherical deconvolution (CSD)) andlearning based methods (e.g., deep learning (DL)) do not explicitly considersuch variabilities in fODF modeling, which consequently leads to inferiorperformance on multi-site and/or longitudinal diffusion studies. In this paper,we propose a novel data-driven deep constrained spherical deconvolution methodto explicitly constrain the scan-rescan variabilities for a more reproducibleand robust estimation of brain microstructure from repeated DW-MRI scans.Specifically, the proposed method introduces a new 3D volumetricscanner-invariant regularization scheme during the fODF estimation. We studythe Human Connectome Project (HCP) young adults test-retest group as well asthe MASiVar dataset (with inter- and intra-site scan/rescan data). TheBaltimore Longitudinal Study of Aging (BLSA) dataset is employed for externalvalidation. From the experimental results, the proposed data-driven frameworkoutperforms the existing benchmarks in repeated fODF estimation. The proposedmethod is assessing the downstream connectivity analysis and shows increasedperformance in distinguishing subjects with different biomarkers.

Robust Fiber ODF Estimation Using Deep Constrained Spherical Deconvolution for Diffusion MRI