DeepSTI: Towards Tensor Reconstruction using Fewer Orientations in Susceptibility Tensor Imaging

The application of STI in human in vivo has been practically infeasible because of its time-consuming acquisition scheme. We propose a novel image reconstruction algorithm for STI that leverages data-driven priors to tackle this issue. Our method, called DeepSTI, learns the data prior implicitly via a deep neural network that resembles the proximal operator of a regularizer function. The dipole inversion problem is then solved iteratively using the learned proximal network. Experimental results demonstrate superior performance of DeepSTI over state-of-the-art methods. DeepSTI is the first reconstruction method to achieve high quality results for human STI with fewer than six orientations.