SHORE based microstructural indices: do they tell us more?

Diffusion weighted magnetic resonance signals convey informationabout tissue microstructure and cytoarchitecture. Inthe last years, many models have been proposed for recoveringthe diffusion signal and extracting information to constitutenew families of microstructural indices. Here we focuson three leading diffusion MRI models: NODDI (NeuriteOrientation Dispersion and Density Imaging), 3D-SHORE(3D Simple Harmonic Oscillator-based Reconstruction andEstimation) and its formulation in the Cartesian space, theMAPMRI (Mean Apparent Propagator MRI) and analyze theinformation conveyed by the respective set of indices basedon information-theoretic measures. This will allow to objectivelyassess the ability of each index of capturing microstructuralfeatures and thus to shade light on their exploitability indiscriminative tasks. To this end, the microstructural descriptorsare treated as machine learning features and analyzedvia information-theoretic methods using in-vivo data. Resultsshow that 3D-SHORE and MAPMARI models provide indiceswith the highest relevance and that the combination ofindices from all models may provide the best ensemble offeatures for classification.