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Abstract #0447

Improving deep unrolled neural networks for radial cine cardiac image reconstruction using memory-efficient training, Conv-LSTM based network

Kanghyun Ryu1, Christopher M. Sandino1, Zhitao Li1, Xucheng Zhu2, Andrew Coristine3, Martin Janich4, and Shreyas S. Vasanawala1
1Stanford University, Stanford, CA, United States, 2GE Healthcare, Menlo Park, CA, United States, 3GE Healthcare, Montreal, QC, Canada, 4GE Healthcare, Munich, Germany

Recently, unrolled neural networks (UNNs) have been shown to improve reconstruction over conventional Parallel Imaging Compressed Sensing (PI-CS) methods for dynamic MR image reconstruction. In this work we propose two methods to improve UNN for Non-Cartesian cardiac cine image reconstruction, namely memory efficient training and Convolutional LSTM based network architecture.The proposed method can significantly improve conventional UNN with higher image quality.

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