Deep constrained spherical deconvolution for robust harmonization

Tianyuan Yao; Francois Rheault; Leon Y. Cai; Vishwesh Nath; Zuhayr Asad; Nancy Newlin; Can Cui; Ruining Deng; Karthik Ramadass; Kurt Schilling; Bennett A. Landman; Yuankai Huo

doi:10.1117/12.2654398

3 April 2023 Deep constrained spherical deconvolution for robust harmonization

Tianyuan Yao, Francois Rheault, Leon Y. Cai, Vishwesh Nath, Zuhayr Asad, Nancy Newlin, Can Cui, Ruining Deng, Karthik Ramadass, Kurt Schilling, Bennett A. Landman, Yuankai Huo

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Proceedings Volume 12464, Medical Imaging 2023: Image Processing; 124640W (2023) https://doi.org/10.1117/12.2654398
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Diffusion weighted magnetic resonance imaging (DW-MRI) captures tissue microarchitecture at a millimeter scale. With recent advantages in data sharing, large-scale multi-site DW-MRI datasets are being made available for multi-site studies. However, DW-MRI suffers from measurement variability (e.g., inter- and intra-site variability, hardware performance, and sequence design), which consequently yields inferior performance on multi-site and/or longitudinal diffusion studies. In this study, we propose a novel, deep learning-based method to harmonize DW-MRI signals for a more reproducible and robust estimation of microstructure. Our method introduces a data-driven scanner-invariant regularization scheme to model a more robust fiber orientation distribution function (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 8 th order spherical harmonics coefficients are employed as data representation. The results show that the proposed harmonization approach maintains higher angular correlation coefficients (ACC) with the ground truth signals (0.954 versus 0.942), while achieves higher consistency of FODF signals for intra-scanner data (0.891 versus 0.826), as compared with the baseline supervised deep learning scheme. Furthermore, the proposed data-driven framework is flexible and potentially applicable to a wider range of data harmonization problems in neuroimaging.

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Tianyuan Yao, Francois Rheault, Leon Y. Cai, Vishwesh Nath, Zuhayr Asad, Nancy Newlin, Can Cui, Ruining Deng, Karthik Ramadass, Kurt Schilling, Bennett A. Landman, and Yuankai Huo "Deep constrained spherical deconvolution for robust harmonization", Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 124640W (3 April 2023); https://doi.org/10.1117/12.2654398

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