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

AxonNet: A self-supervised Deep Neural Network for Intravoxel Structure Estimation from DW-MRI