Meeting Banner
Abstract #3100

Deep Learning Reconstruction improves CEST MRI

Shu Zhang1, Xinzeng Wang2, F. William Schuler1, R. Marc Lebel3, Mitsuharu Miyoshi4, Ersin Bayram2, Elena Vinogradov5, Jason M. Johnson6, Jingfei Ma7, and Mark D. Pagel1
1Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, 2Global MR Applications & Workflow, GE Healthcare, Houston, TX, United States, 3Global MR Applications & Workflow, GE Healthcare, Calgary, AB, Canada, 4Global MR Applications & Workflow, GE Healthcare Japan, Tokyo, Japan, 5Radiology, UT Southwestern Medical Center, Dallas, TX, United States, 6Neuroradiology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, 7Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States

Image reconstruction using deep learning (DL Recon) is capable of enhancing image signal-to-noise ratio (SNR) without losing image resolution or altering the image contrast. Our study demonstrates that CEST imaging and quantification, which are often limited by SNR and long scan time, can be improved with DL Recon. Our results clearly indicated that DL Recon can be used for CEST imaging with higher spatial resolution without or with only a mild increase in scan time or for CEST imaging in reduced scan time by using parallel imaging without the typical SNR penalty.

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

Join Here