Abstract #3627

Partial Fourier MRI Reconstruction Using Convolutional Neural Networks

Peibei Cao^1,2, Linfang Xiao^1,2, Yilong Liu^1,2, Yujiao Zhao^1,2, Yanqiu Feng³, Alex T Leong^1,2, and Ed X Wu^1,2

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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