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 Pagel^1,7

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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