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Multi-compartment modeling of diffusion-weighted magnetic resonance imagingmeasurements is necessary for accurate brain connectivity analysis. Existingmethods for estimating the number and orientations of fascicles in an imagingvoxel either depend on non-convex optimization techniques that are sensitive toinitialization and measurement noise, or are prone to predicting spuriousfascicles. In this paper, we propose a machine learning-based technique thatcan accurately estimate the number and orientations of fascicles in a voxel.Our method can be trained with either simulated or real diffusion-weightedimaging data. Our method estimates the angle to the closest fascicle for eachdirection in a set of discrete directions uniformly spread on the unit sphere.This information is then processed to extract the number and orientations offascicles in a voxel. On realistic simulated phantom data with known groundtruth, our method predicts the number and orientations of crossing fasciclesmore accurately than several existing methods. It also leads to more accuratetractography. On real data, our method is better than or compares favorablywith standard methods in terms of robustness to measurement down-sampling andalso in terms of expert quality assessment of tractography results.

A machine learning-based method for estimating the number and orientations of major fascicles in diffusion-weighted magnetic resonance imaging