Nonlinear Susceptibility Inversion Deep Learning Model for Robust Quantitative Susceptibility Mapping

Motivation: Quantitative susceptibility mapping (QSM) estimates the spatial distribution of tissue susceptibility by solving a challenging ill-posed dipole inversion problem, which heavily affects the accuracy of tissue susceptibility quantification.Goal(s): To generate high-quality QSM images.Approach: In this study, we present a deep learning method for susceptibility inversion that utilizes a nonlinear susceptibility inversion model, NSIDL.Our approach integrated the Proximal Gradient Descent (PGD)[1] algorithm and embedding the physical model in the network. Results: NSIDL was compared to traditional and deep learning methods, and it was found that NSIDL can effectively suppress streaking artifacts, mitigate noise amplification, and prevent excessive smoothing.Impact: This study introduced the NSIDL deep learning method, which improved the accuracy of tissue magnetic sensitivity quantification. The improvement of QSM performance can help clinical doctors make more informed decisions based on reliable sensitivity measurements.