STI-net: reconstruction of the susceptibility tensor using deep neural network

Motivation: When solving the Susceptibility Tensor Imaging problem, fast algorithms based on Least Squares require an elevated number of acquisitions, while more robust solvers that use DTI information may produce over-smoothed solutions. Goal(s): To create a deep neural network based reconstruction algorithm to produce high SNR STI images with a reduced number of MRI acquisitions. Approach: Use a physics-informed deep neural network approach, trained with various geometrical objects, capable of accurately reconstructing susceptibility tensors. Results: We obtained susceptibility tensors with the expected anisotropy, better alignment with DTI eigenvectors and high SNR. Impact: Our STI-net algorithm is capable of reconstructing accurate STI images with higher SNR, compared with traditional algorithms.