Yuan Lian1, Xinyu Ye1, Yajing Zhang2, and Hua Guo1
1Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, 2MR Clinical Science, Philips Healthcare, Suzhou, China
1Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, 2MR Clinical Science, Philips Healthcare, Suzhou, China
We design a deep learning model Joint-ISTA-Net, which exploits the group sparsity of multi-contrast MR images in our model to improve the reconstruction quality of Compressed Sensing.
Fig
1. Structure of proposed Joint-ISTA-Net and a iterative Share Block. In Share
Block, three 3x3 CNN layers with Relu stands forward sparse transformation.
Figures in transform domain pass through both individual and joint soft
threshold function to be denoised, and then decoded back to image domain. Here
joint soft threshold function is implemented to exploit the group sparsity
property of multi-contrast MR images.
Fig
2. Comparison of reconstruction method with R=10. Proposed Joint-ISTA-Net has
advantages on feature preserving and provides sharper edge.
