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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