Abstract #3541

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

Li Huang¹, Xueming Zou^1,2,3, and Tao Zhang^1,2,3

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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