Keywords: Susceptibility/QSM, Quantitative Susceptibility mapping, LoopNet, Bidirectional U-net, QSM dipole inversion
Motivation: Most current deep learning (DL) QSM methods were developed based on U-net, whose performances might not be sufficiently good.
Goal(s): To proposed a new network baseline for deep learning QSM methods development.
Approach: We developed a LoopNet, by applying the proposed bidirectional loop and a self-tailored GHPA attention module into a Unet backbone, making better use of the latent information in deep networks.
Results: Simulated and in vivo experiments showed that the propoed LoopNet led to improved results than U-net.
Impact: This work introduces a novel deep neural network backbone, allowing researchers to develop innovative QSM methods easily by upgrading their original U-net to LoopNet, thanks to the plug-and-play design.
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