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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