Abstract #1530
Deep learning based image reconstruction for improved 3D multiparameter mapping and synthetic MR imaging
Ken-Pin Hwang1, Kim O. Learned2, Naoyuki Takei3, R. Marc Lebel4, Peter Johansson5, David Shin6, Xinzeng Wang7, Catharina Petersen5, Marcel Warntjes5, Suchandrima Banerjee6, and Linda Chi2
1Department of Imaging Physics, The University of Texas M.D. Anderson Cancer Center, Houston, TX, United States, 2Department of Neuroradiology, The University of Texas M.D. Anderson Cancer Center, Houston, TX, United States, 3GE Healthcare, Hino, Japan, 4Global MR Applications and Workflow, GE Healthcare, Calgary, AB, Canada, 5SyntheticMR, AB, Linkoping, Sweden, 6GE Healthcare, Menlo Park, CA, United States, 7GE Healthcare, Houston, TX, United States
Synopsis
Brain data with high grade glioma were acquired with a 3D multiparameter mapping sequence and reconstructed with a novel deep learning based method (DL Recon). Synthetic images created from maps fitted to the reconstructed images were rated by experienced radiologists for image quality and diagnostic utility. Images from the DL Recon workflow consistently rated equal to or better than those created from conventional reconstruction. We find that the SNR and resolution benefits of 3D DL Recon extend to improve the resulting relaxation maps and subsequent synthetic images.
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