Fast identification of optimal fascicle configurations from standard clinical diffusion MRI using Akaike information criterion

Analytic multi-compartment models have gained a tremendous popularity in the recent literature for studying the brain white matter microstructure from diffusion MRI. This class of models require the number of compartments to be known in advance. In the white matter however, several non-collinear bundles of axons, termed fascicles, often coexist in a same voxel. Determining the optimal fascicle configuration is a model selection problem. In this paper, we aim at proposing a novel approach to identify such a configuration from clinical diffusion MRI where only few diffusion images can be acquired and time is of the essence. Starting from a set of fitted models with increasing number of fascicles, we use Akaike information criterion to estimate the probability of each candidate model to be the best Kullback-Leibler model. These probabilities are then used to average the different candidate models and output an MCM with optimal fascicle configuration. This strategy is fast and can be adapted to any multi-compartment model. We illustrate its implementation with the ball-and-stick model and show that we obtain better results on single-shell low angular resolution diffusion MRI, compared to the state-of-the-art automatic relevance detection method, in a shorter processing time.