A Deconvolution Approach Based on Multi-Tensor Model to Solve Fiber Crossing in Diffusion-MRI

A deconvolution approach, based on a multi-tensor model, is presented to solve fiber crossing in diffusion MRI. In order to provide a direct physical interpretation of the signal generation process, we re-wrote the classical multi-tensor model, identifying a significant scalar parameter alpha to characterize the deconvolution process. Simulations show that, in presence of noise, the method is able to correctly separate fiber crossing. Application on in-vivo data highlights the ability of our approach to distinguish more than two fibers within the same voxel, suggesting its application in fiber tracking or connectivity studies even of complex brain structures