A machine learning-based method for estimating the number and orientations of major fascicles in diffusion-weighted magnetic resonance imaging
This work addresses a specific bottleneck in neuroimaging for researchers and clinicians by providing a more reliable tool for brain connectivity mapping, though it is incremental as it builds on existing multi-compartment modeling approaches.
The paper tackles the problem of accurately estimating the number and orientations of fascicles in diffusion-weighted MRI, which is crucial for brain connectivity analysis, by proposing a machine learning-based method that outperforms existing techniques in simulations and shows improved robustness and tractography accuracy on real data.
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.