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