LAUSR

The purpose of the current study was to develop deep learning‐regularized, single‐step quantitative susceptibility mapping (QSM) quantification, directly generating QSM from the total phase map. A deep learning‐regularized, single‐step QSM quantification model, named SS‐POCSnet, was trained with datasets created using the QSM synthesis approach in QSM reconstruction challenge 2.0. In SS‐POCSnet, a data fidelity term based on a single‐step model was iteratively applied that combined the spherical mean value kernel and dipole model. Meanwhile, SS‐POCSnet regularized susceptibility maps, avoiding underestimating susceptibility values. We evaluated the SS‐POCSnet on 10 synthetic datasets, 24 clinical datasets with lesions of cerebral microbleed (CMB) and calcification, and 10 datasets with multiple sclerosis (MS).On synthetic datasets, SS‐POCSnet showed the best performance among the methods evaluated, with a normalized root mean squared error of 37.3% ± 4.2%, susceptibility‐tuned structured similarity index measure of 0.823 ± 0.02, high‐frequency error norm of 37.0 ± 5.7, and peak signal‐to‐noise ratio of 42.8 ± 1.1. SS‐POCSnet also reduced the underestimations of susceptibility values in deep brain nuclei compared with those from the other models evaluated. Furthermore, SS‐POCSnet was sensitive to CMB/calcification and MS lesions, demonstrating its clinical applicability. Our method also supported variable imaging parameters, including matrix size and resolution. It was concluded that deep learning‐regularized, single‐step QSM quantification can mitigate underestimating susceptibility values in deep brain nuclei.