LoopNet: A New Baseline Network for 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.