  We present a method for estimating intravoxel parameters from a DW-MRI based
on deep learning techniques. We show that neural networks (DNNs) have the
potential to extract information from diffusion-weighted signals to reconstruct
cerebral tracts. We present two DNN models: one that estimates the axonal
structure in the form of a voxel and the other to calculate the structure of
the central voxel using the voxel neighborhood. Our methods are based on a
proposed parameter representation suitable for the problem. Since it is
practically impossible to have real tagged data for any acquisition protocol,
we used a self-supervised strategy. Experiments with synthetic data and real
data show that our approach is competitive, and the computational times show
that our approach is faster than the SOTA methods, even if training times are
considered. This computational advantage increases if we consider the
prediction of multiple images with the same acquisition protocol.
