Learning compact q-space representations for multi-shell diffusion-weighted MRI
Christiaens D, Cordero-Grande L, Hutter J, Price AN, Deprez M, Hajnal JV, Tournier JD (2019)
Publication Type: Journal article
Publication year: 2019
Journal
Book Volume: 38
Pages Range: 834-843
Article Number: 8481703
Journal Issue: 3
Abstract
Diffusion-weighted MRI measures the direction and scale of the local diffusion process in every voxel through its spectrum in q-space, typically acquired in one or more shells. Recent developments in microstructure imaging and multi-tissue decomposition have sparked renewed attention in the radial b-value dependence of the signal. Applications in motion correction and outlier rejection, therefore, require a compact linear signal representation that extends over the radial as well as angular domain. Here, we introduce SHARD, a data-driven representation of the q-space signal based on spherical harmonics and a radial decomposition into orthonormal components. This representation provides a complete, orthogonal signal basis, tailored to the spherical geometry of q-space, and calibrated to the data at hand. We demonstrate that the rank-reduced decomposition outperforms model-based alternatives in human brain data, while faithfully capturing the micro- and meso-structural information in the signal. Furthermore, we validate the potential of joint radial-spherical as compared with single-shell representations. As such, SHARD is optimally suited for applications that require low-rank signal predictions, such as motion correction and outlier rejection. Finally, we illustrate its application for the latter using outlier robust regression.
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United Kingdom (GB)How to cite
APA:
Christiaens, D., Cordero-Grande, L., Hutter, J., Price, A.N., Deprez, M., Hajnal, J.V., & Tournier, J.-D. (2019). Learning compact q-space representations for multi-shell diffusion-weighted MRI. IEEE Transactions on Medical Imaging, 38(3), 834-843. https://doi.org/10.1109/TMI.2018.2873736
MLA:
Christiaens, Daan, et al. "Learning compact q-space representations for multi-shell diffusion-weighted MRI." IEEE Transactions on Medical Imaging 38.3 (2019): 834-843.
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