Keywords: Susceptibility/QSM, Susceptibility, Susceptibility Tensor Imaging
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.
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