title: Learning a reliable estimate of the number of fiber directions in diffusion MRI.
abstract: Having to determine an adequate number of fiber directions is a fundamental limitation of multi-compartment models in diffusion MRI. This paper proposes a novel strategy to approach this problem, based on simulating data that closely follows the characteristics of the measured data. This provides the ground truth required to determine the number of directions that optimizes a formal measure of accuracy, while allowing us to transfer the result to real data by support vector regression. The method is shown to result in plausible and reproducible decisions on three repeated scans of the same subject. When combined with the ball-and-stick model, it produces directional estimates comparable to constrained spherical deconvolution, but with significantly smaller variance between re-scans, and at a reduced computational cost.
authors.affiliation: Max Planck Institute for Intelligent Systems, Tübingen, Germany.
authors.forename: Thomas
authors.initials: T
authors.lastname: Schultz
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000166:cytology*; D063132:Connectome / Q000379:methods*; D056324:Diffusion Tensor Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D009413:Nerve Fibers, Myelinated / Q000648:ultrastructure*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity