A framework for ODF inference by using fiber tract adaptive MPG selection

Abstract

The authors propose a method that selects a set of motion probing gradient (MPG) directions, which is adapted for measuring fiber tracts in some specific region of interest (ROI) with smaller number of MPGs. Given a training set of diffusion magnetic resonance (MR) images, the method selects the set of MPG directions by minimizing a cost function, which represents the square errors of the reconstructed oriented distribution functions (ODFs). This selection of MPGs is a combinatorial optimization problem, and a simulated annealing scheme is employed for selecting the MPGs. Experimental results demonstrated that the set of MPG directions selected by our proposed method reconstructed the ODFs more accurately than an existing method based on spherical harmonics and on greedy optimization.