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

TL;DR

This paper introduces a machine learning approach for accurately estimating the number and orientations of brain fascicles in diffusion MRI data, improving robustness and accuracy over existing methods.

Contribution

The paper presents a novel machine learning-based method that estimates fascicle number and orientations from diffusion MRI, outperforming traditional optimization-based techniques.

Findings

More accurate fascicle estimation on simulated data

Improved tractography results on real data

Enhanced robustness to measurement noise and down-sampling

Abstract

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 and orientations of fascicles in a voxel. Our method can be trained with either simulated or real diffusion-weighted imaging data. Our method estimates the angle to the closest fascicle for each direction in a set of discrete directions uniformly spread on the unit sphere. This information is then processed to extract the number and orientations of fascicles in a voxel. On realistic simulated phantom data with known ground truth, our method predicts the number and orientations of crossing fascicles more accurately than several existing methods. It also leads to more accurate tractography. On real data, our method is better than or compares favorably with standard methods in terms of robustness to measurement down-sampling and also in terms of expert quality assessment of tractography results.