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Abstract #1977

Joint-ISTA-Net: A model-based deep learning network for multi-contrast CS-MRI reconstruction

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

Compressed Sensing theory is often applied to accelerate the acquisition of multi-contrast MR images. When highly undersampled, CS-MRI suffers from non-negligible reconstruction error. Here we propose an unrolled iterative deep-learning model to further utilize the group sparsity property for multi-contrast MRI reconstruction at high acceleration factor, named Joint-ISTA-Net, to reduce reconstruction error and aliasing. Our method adds a joint-shrinkage-thresholding model into ISTA-Net to generate a better reconstruction for multi-contrast image pairs. Experiments show the effectiveness of the proposed strategy.

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