Interventional MRI (i-MRI) needs fast data acquisition and image reconstruction. We have shown that a Low-rank and Sparsity decomposition with Framelet transform model with Primal dual fixed point optimization (LSFP) could satisfy the reconstruction of real-time i-MRI. In this study, we unrolled the LSFP into a deep neural network, dubbed LSFP-Net, with multi-coil golden-angle radial sampling. Simulation results showed that LSFP-Net outperformed the state-of-the-art methods, and a phantom experiment demonstrated its potential for real-time i-MRI.
This abstract and the presentation materials are available to members only; a login is required.