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Abstract #3541

A Lightweight deep learning network for MR image super-resolution using separable 3D convolutional neural networks

Li Huang1, Xueming Zou1,2,3, and Tao Zhang1,2,3
1School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China, 2Key Laboratory for Neuroinformation, Ministry of Education, Chengdu, China, 3High Field Magnetic Resonance Brain Imaging Laboratory of Sichuan, Chengdu, China

The existing deep learning networks for MR super-resolution image reconstruction using standard 3D convolutional neural networks typically require a huge amount of parameters and thus excessive computational complexity. This has restricted the development of deeper neural networks for better performance. Here we propose a lightweight separable 3D convolution neural network for MR image super-resolution. Results show that our method can not only greatly reduce the amount of parameters and computational complexity but also improve the performance of image super-resolution.

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