Unsupervised multi-tissue decomposition of single-shell diffusion-weighted imaging by generalization to multi-modal data

Introduction In recent years, data-driven analysis of diffusion-weighted imaging (DWI) has been extended beyond white matter (WM), explicitly modelling partial voluming with adjacent tissues. Supervised methods such as singleand multi-tissue constrained spherical deconvolution (CSD) reconstruct orientation distribution functions (ODF) of WM, grey matter (GM), and cerebrospinal fluid (CSF), given response functions (RF) for these tissues. These RFs are calibrated to the data based on prior segmentations, either obtained from T1-weighted anatomical data2 or directly from DWI. Alternatively, unsupervised methods decompose DWI data in tissue components, akin to blind source separation, jointly optimizing tissue RFs and ODFs based on sparsity or convexity constraints.