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A machine learning-based method for estimating the number and orientations of major fascicles in diffusion-weighted magnetic resonance imaging
[article]
2020
arXiv
pre-print
Multi-compartment modeling of diffusion-weighted magnetic resonance imaging measurements is necessary for accurate brain connectivity analysis. Existing methods for estimating the number and orientations of fascicles in an imaging voxel either depend on non-convex optimization techniques that are sensitive to initialization and measurement noise, or are prone to predicting spurious fascicles. In this paper, we propose a machine learning-based technique that can accurately estimate the number
arXiv:2006.11117v1
fatcat:soplx5sydnf3xlzwsxa6v7znca