Constrained Dipole Inversion Using Jointly Learned Local Field and Susceptibility Priors

The inversion of tissue magnetic susceptibility from a single-orientation phase measurement is an ill-posed problem. In this work, we propose a novel learning-based constrained reconstruction to integrate the jointly learned tissue field and susceptibility priors with the physics-based dipole inversion formalism such that the non-Gaussian model biased caused by the substantial errors in the estimated tissue field and the streaking artifacts in the tissue susceptibility can be effectively reduced. An efficient solver was also developed to solve the optimization problem. We demonstrated the superior performance of the proposed method over the state-of-the-art dipole inversion method using in vivo datasets.