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
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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