Deep Quantitative Susceptibility Mapping for Background Field Removal and Total Field Inversion

Abstract

Quantitative susceptibility mapping (QSM) utilizes MRI signal phase to estimate local tissue susceptibility, which has been shown useful to provide novel image contrast and as biomarkers of abnormal tissue. QSM requires addressing a challenging post-processing problem: filtering of image phase estimates and inversion of the phase to susceptibility relationship. A wide variety of quantification errors, robustness limitations, and artifacts constraints QSM clinical translation. To overcome these limitations, a robust deep-learning-based QSM reconstruction approach is proposed to perform background field removal and susceptibility inversion simultaneously from input MRI phase images. Synthetic training data based on in-vivo data sources and physics simulations were used for training. The network was quantitatively tested using gold-standard in-silico labeled dataset against established background field removal and QSM inversion approaches. In addition, the algorithm was applied to a QSM challenge data and clinical susceptibility-weighted imaging (SWI) data. When quantitatively compared against gold-standard in-silico labels, the proposed algorithm outperformed the existing comparable background field removal approaches and QSM reconstruction algorithms. The QSM challenge data and clinical SWI data demonstrated that the proposed approach was able to robustly generate high quality local field and QSM with improved accuracy.