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Abstract #4755

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

Zuojun Wang1, Henry Ka-Fung Mak1, and Peng Cao1
1Department of Diagnostic Radiology, The University of HongKong, Hong Kong, China


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.

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