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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