Diffusion-weighted magnetic resonance imaging (DW-MRI) offers a unique insight on microarchitecture of the in-vivo human brain. Multiple well-known reconstruction methods that model geometrical and micro-structural properties of the tissue such as multi-tissue constrained spherical deconvolution (MT-CSD) and spherical mean technique (SMT) rely on high quality acquisitions (more than 2 shells and 45 gradient directions) which is a constraint. We propose recovery of fiber-ODFs, compartment diffusivities and volume-fractions using a two-stage deep learning framework by training on human-connectome-project dataset. The proposed approach can predict fiber-ODFs using single shell DW-MRI on a tumor patient and assess the diseased region of interest.
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