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Abstract #2804

Reconstruction in deep learning of highly under-sampled T2-weighted image with T1- weighted image

Lei Xiang1, Weitang Chang2, Yong Chen2, Weili Lin2, Qian Wang1, and Dinggang Shen2

1School of Biomedical Engineering, Shanghai Jiao Tong University, shanghai, China, 2Department of Radiology and BRIC, University of North Carolina at Chapel Hill, chapel hill, NC, United States

T1-weighted image (T1WI) and T2-weighted image (T2WI) are routinely acquired in MRI protocols, which can provide complementary information to each other. However, the acquisition time for each sequence is non-trivial, making clinical MRI a slow and expensive procedure. With the purpose to shorten MRI acquisition time, we present a deep learning approach to reconstruct T2WI from T1WI and highly under-sampled T2WI. Our results demonstrate that the proposed method could achieve 8 or higher acceleration rate while keeping high image quality of the reconstructed T2WI.

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