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Abstract #4688

A Deep Unrolled Network for Reconstruction of Real-time Interventional MRI with Multi-coil Radial Sampling

Zhao He1, Ya-Nan Zhu2, Yuchen He2, Yu Chen1, Suhao Qiu1, Linghan Kong1, Xiaoqun Zhang2, and Yuan Feng1
1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 2School of Mathematical Sciences, MOE-LSC and Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, China


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.

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