Synthesize Quantitative Susceptibility Mapping from Susceptibility Weighting Imaging Using a Cycle Generative Adversarial Network

Abstract

Quantitative susceptibility mapping (QSM) obtained from the MRI phase images is valuable in neurological disease diagnoses. Meanwhile, the role of thumb MRI scan probing susceptibility contrast is susceptibility weighting imaging (SWI), which might contain blooming artifacts that would affect the hypointensity appearance. Many conventional methods have been developed for QSM reconstruction, including the deep learning-based approach that is applicable in clinical diagnoses. Here, we apply the cycle generative adversarial network with a perceptual loss to synthesize QSM images from SWI images. The predicted QSM images showed their application in brain microbleed detection.