Abstract #4301

Memory-friendly and Robust Deep Learning Architecture for Accelerated MRI

Zi Wang¹, Chen Qian¹, Di Guo², Hongwei Sun³, Rushuai Li⁴, and Xiaobo Qu¹

¹Department of Electronic Science, Biomedical Intelligent Cloud R&D Center, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China, ²School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China, ³United Imaging Research Institute of Intelligent Imaging, Beijing, China, ⁴Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China

Synopsis

Deep learning has shown astonishing performance in accelerated MRI. Most methods adopt the convolutional neural network and perform 2D convolution since many MR images or their corresponding k-space are in 2D. In this work, we try a different approach that explores the memory-friendly 1D convolution, making the deep network easier to be trained and generalized. Furthermore, a one-dimensional deep learning architecture (ODL) is proposed for MRI reconstruction. Results demonstrate that, the proposed ODL provides improved reconstructions than state-of-the-art methods and shows nice robustness to some mismatches between the training and test data.

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