Deep-Learning-regularized Single-step Quantitative Susceptibility Mapping (QSM) Quantification

We develop a deep-learning-regularized single-step QSM quantification to generate QSM directly from the total phase map. A deep-learning-regularized dipole inversion network, named POCSnet, was deployed to a single-step QSM (SS-POCSnet) network, which combined a variable-SHARP (VSHARP) and the POCSnet. Meanwhile, SS-POCSnet showed improved accuracy compared with conventional single-step QSM methods. We also demonstrated the generalizability of SS-POCSnet on different datasets in vivo.