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Abstract #3627

Partial Fourier MRI Reconstruction Using Convolutional Neural Networks

Peibei Cao1,2, Linfang Xiao1,2, Yilong Liu1,2, Yujiao Zhao1,2, Yanqiu Feng3, Alex T Leong1,2, and Ed X Wu1,2
1Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong, China, 2Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China, 3School of Biomedical Engineering, Southern Medical University, Guangzhou, China

Convolutional neural network (CNN) has emerged as a powerful tool for medical image reconstruction. In this study, we designed and implemented a CNN model for partial Fourier MRI reconstruction, and compared its performance with the existing projection onto convex sets (POCS) method. The results demonstrated that our proposed deep learning approach could effectively recovered the high frequency components and outperformed the POCS method especially when partial Fourier fraction is close to 50%.

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