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Abstract #3479

Isotropic MRI Reconstruction with 3D Convolutional Neural Network

Xiaole Zhao1, Tian He1, Ying Liao1, Yun Qin1, Tao Zhang1,2,3, and Mark Zou1,2,3
1School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China, 2High Field Magnetic Resonance Brain Imaging Laboratory of Sichuan, Chengdu, China, 3Key Laboratory for NeuroInformation of Ministry of Education, Chengdu, China

Typical magnetic resonance imaging (MRI) usually shows distinct anisotropic spatial resolution in imaging plane and slice-select direction. Image super-resolution (SR) techniques are widely used as an alternative method to isotropic MRI reconstruction. In this work, we propose to reconstruct isotropic magnetic resonance (MR) volumes via 3D convolutional neural network in an end-to-end manner. 3D SRCNN is utilized to preliminarily validate the idea and it produces quantitative and qualitative results significantly superior to traditional methods, such as Cube-Avg and NLM methods.

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