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Abstract #3433

pISTA-SENSE-ResNet for Parallel MRI Reconstruction

Tieyuan Lu1, Xinlin Zhang1, Yihui Huang1, Di Guo2, and Xiaobo Qu1
1Department of Electronic Science, Xiamen University, Xiamen, China, 2School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China

Magnetic resonance imaging has been widely applied in clinical diagnosis, however, is limited by its long data acquisition time. Although imaging can be accelerated by sparse sampling and parallel imaging, achieving promising reconstruction images with a fast reconstruction speed remains a challenge. In this work, we design the neural network structure from the perspective of sparse iterative reconstruction. The experimental results of a public knee dataset show that compared with the optimization-based method and the latest deep learning parallel imaging methods, the proposed network has less error in reconstruction and is more stable under different acceleration factors.

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