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Abstract #4667

Reconstruction of high undersampling rate images using a cascade of convolutional neural networks

Xi Chen1, Shuo Chen2, and Rui Li2

1Beijing Institute of Technology, Beijing, China, 2Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China

Imaging speed is important in many magnetic resonance imaging (MRI) applications because long scan time increases the risk of artifacts. At present, reconstruction method based on compressed sensing and deep learning significantly increases the speed of MRI scan. However, the performance of current models is not good at high undersampling rate. Here we used a large dataset to improve the undersampling rate of a CNN based MR reconstruction while maintaining high image quality. Our results showed an average 2.6% root-mean-square error in reconstructing from 16-fold undersampling k-space, which outperforms traditional method.

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