Ruimin Feng1, Yuting Shi1, Jie Feng1, Yuyao Zhang2, and Hongjiang Wei1
1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 2School of Information Science and Technology, ShanghaiTech University, Shanghai, China
1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 2School of Information Science and Technology, ShanghaiTech University, Shanghai, China
We proposed a model-based generative adversarial network for quantitative susceptibility mapping. It provided superior image quality and accuracy quantification compared to recently developed QSM reconstruction methods.
Figure 1. The schematic diagram of MoG-QSM. Blue blocks play the role of the proximal operator. They are shared weights and replace with convolutional neural network. The output of each generator performs a physical model operation (green block). The final output and label are feed into the discriminator to distinguish if the image is true or not.
Figure 2. Comparison of different QSM reconstruction methods on a healthy subject. The red arrow pointed to the blur cortex susceptibility contrast reconstructed by the LPCNN.