Shore-based microstructural indices: do they tell us more?
Recent methods for diffusion weighted magnetic resonance convey information about tissue microstructure. In the last years, many models have been proposed for recovering the diffusion signal and extracting information to constitute new families of microstructural indices. Here we focus on three leading diffusion MRI models: NODDI (Neurite Orientation Dispersion and Density Imaging), 3D-SHORE (3D Simple Harmonic Oscillator-based Reconstruction and Estimation) and its formulation in the Cartesian space, the MAPMRI (Mean Apparent Propagator MRI) and analyze the information conveyed by the respective set of indices based on information-theoretic measures. This will allow to objectively assess the ability of each index of capturing microstructural features and thus to shed light on their exploitability in discriminative tasks. To this end, the microstructural descriptors are treated as machine learning features and analyzed via information-theoretic methods. First results on in-vivo data suggest that 3D-SHORE and MAPMARI could be more eloquent in describing microstructure and that a combination of descriptors obtained from all models may provide the best subset of features for a classification task.