Pu-Yeh Wu1, Weiqiang Dou1, Hongyuan Ding2, Jiulou Zhang3, Yong Shen1, Guangnan Quan1, Zhangxuan Hu1, and Bing Wu1
1GE Healthcare, Beijing, China, 2Radiology Department, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China, 3Artificial Intelligence Imaging Laboratory, School of Medical Imaging, Nanjing Medical University, Nanjing, China
1GE Healthcare, Beijing, China, 2Radiology Department, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China, 3Artificial Intelligence Imaging Laboratory, School of Medical Imaging, Nanjing Medical University, Nanjing, China
We proposed a deep learning-based method for super-resolution DWI reconstruction using SRGAN and multi-shot DWI as target. Our preliminary results demonstrated that the proposed method could provide perceptually convincing super-resolution and distortion-corrected DWI images.
Figure 3. Representative T2WI, DWI, MUSE, and SRGAN reconstructed images.
Figure 4. Detailed comparison among DWI, MUSE, SRResNet, and SRGAN reconstructed images.