Meeting Banner
Abstract #1876

Deep learning based image reconstruction for improved multiparameter mapping and synthetic MRI

Ken-Pin Hwang1, Xinzeng Wang2, Marc Lebel2, Peter Johansson3, Catharina Petersen3, Marcel Warntjes3, Ersin Bayram2, Suchandriam Banerjee2, Jingfei Ma1, and Jason M Johnson4
1Department of Imaging Physics, The University of Texas M.D. Anderson Cancer Center, Houston, TX, United States, 2MR Applications and Workflow, GE Healthcare, Waukesha, WI, United States, 3SyntheticMR, Linkoping, Sweden, 4Department of Radiology, The University of Texas M.D. Anderson Cancer Center, Houston, TX, United States

Images from a multiparameter mapping sequence were reconstructed with a novel deep learning based reconstruction (DL Recon) method trained to remove noise and enhance edges. Mean T1, T2, and PD values as measured in a system phantom differed by less than 0.6% between the DL and conventional reconstructions, while noise was lower in all measurements on DL Recon images. In vivo synthetic images also exhibited reduced noise and increased definition of structures. We find that the SNR and resolution benefits of DL Recon applied to raw MR data extend to improve the fitted relaxation maps and subsequent synthetic images.

This abstract and the presentation materials are available to 2020 meeting attendees and eLibrary customers only; a login is required.

Join Here