Human brain tissue microstructure characterization using 3D-SHORE on the HCP data

The Human Connectome Project (HCP)1 data is composed of high resolution multi-shell diffusion weighted imaging acquisitions. Continuous analytical reconstruction models, such as the 3D Simple Harmonic Oscillator based Reconstruction and Estimation2 (3D-SHORE), are able to process multi-shell acquisition natively and give an analytical representation of the Ensemble Average Propagator (EAP). From the EAP, it is then possible to retrieve information about the water molecules displacement like the Orientation Distribution Function (ODF), necessary to perform brain tractography, but also other scalar indices that can provide accurate estimation of microstructural properties of the brain tissues. A large number of studies have been conducted on the HCP data targeting the extraction of fibers orientation information, while the assessment of tissue microstructural properties on this data is still unexplored. In this work, microstructural features are inferred from the reconstruction of the HCP data using the 3DSHORE model. The related numerical measures3 are: i) Return To the Origin Probability (RTOP), ii) Return To the Axis Probability (RTAP) and iii) Return To the Plane Probability (RTPP), which are calculated and used for assessing the potential of multi-shell acquisitions for characterizing human brain tissues.