Jun Lyu1, Chengyan Wang2, and Guang Yang3,4
1School of Computer and Control Engineering, Yantai University, Yantai, China, 2Human Phenome Institute, Fudan University, Shanghai, China, 3Cardiovascular Research Centre, Royal Brompton Hospital, London, United Kingdom, 4National Heart and Lung Institute, Imperial College London, London, United Kingdom
1School of Computer and Control Engineering, Yantai University, Yantai, China, 2Human Phenome Institute, Fudan University, Shanghai, China, 3Cardiovascular Research Centre, Royal Brompton Hospital, London, United Kingdom, 4National Heart and Lung Institute, Imperial College London, London, United Kingdom
To demonstrate the feasibility of combining parallel imaging (PI) with the generative adversarial network (GAN) for accelerated multi-channel MRI reconstruction. In our proposed PIC-GAN framework, we used a progressive refinement method in both frequency and image domains.
Figure 1. Schema of the proposed parallel imaging and generative adversarial network (PIC-GAN) reconstruction network.
Figure 2. Representative reconstructed abdominal images with acceleration AF= 6. The 1st and 2nd rows depict reconstruction results for regular Cartesian sampling, the 3rd and 4th row depict the same for variable density random sampling. The PIC-GAN reconstruction shows reduced artifacts compared to other methods.