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Abstract #1457

Deep Learning-based image reconstruction improves CEST MRI

Shu Zhang1, Xinzeng Wang2, F. William Schuler1, R. Marc Lebel3, Mitsuharu Miyoshi4, Ersin Bayram2, Elena Vinogradov5, Jason Michael Johnson6, Jingfei Ma7, and Mark David Pagel1,7
1Cancer Systems Imaging, 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, MD Anderson Cancer Center, Houston, TX, United States, 7Imaging Physics, MD Anderson Cancer Center, Houston, TX, United States

Chemical exchange saturation transfer (CEST) measurements can be compromised by a low signal-to-noise ratio (SNR) due to the small CEST contrast in vivo. Deep learning-based image reconstruction (DL Recon) can enhance image SNR without losing image resolution or altering the image contrast, hence has the potential to improve quantitative CEST measurements. In this study, we investigated the improvement to CEST quantitation by DL Recon in glioma patients. We found that DL Recon substantially reduced the noise in the MTRasym maps and improved the lesion conspicuity.

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