Abstract #0727

Free-breathing T2 mapping of the abdomen in half the scan time using RADTSE with deep learning reconstruction

Brian Toner¹, Simon Arberet², Eze Ahanonu³, Ute Goerke⁴, Kevin Johnson⁵, Fei Han⁶, Shu Zhang⁷, Diego Martin⁷, Vibhas Deshpande⁸, Mariappan Nadar², Ali Bilgin^3,9, and Maria Altbach^5,9

¹Applied Mathematics, University of Arizona, Tucson, AZ, United States, ²Digital Technology & Innovation, Siemens Healthineers, Princeton, NJ, United States, ³Electrical & Computer Engineering, University of Arizona, Tucson, AZ, United States, ⁴Siemens Healthineers, Tucson, AZ, United States, ⁵Medical Imaging, University of Arizona, Tucson, AZ, United States, ⁶Siemens Healthineers, Los Angeles, CA, United States, ⁷Radiology, Houston Methodist Research Institute, Houston, TX, United States, ⁸Siemens Healthineers, Austin, TX, United States, ⁹Biomedical Engineering, University of Arizona, Tucson, AZ, United States

Synopsis

Keywords: Liver, Image Reconstruction

Motivation: Free-breathing T2 mapping of the abdomen is possible for subjects that cannot hold their breath, but current techniques require long scan times that are not always possible.

Goal(s): To produce high quality T2 weighted images and T2 parameter map from highly accelerated scans of the abdomen.

Approach: Combining the radial turbo spin echo sequence, navigator triggering, and new deep learning reconstruction techniques to increase the acceleration of the acquisition while maintaining image quality.

Results: Using these techniques, one can produce high quality T2 weighted images and T2 parameter map of the entire abdomen in under 4 minutes.

Impact: Deep learning techniques significantly reduce both scan time and reconstruction time for highly accelerated, navigator-triggered free breathing T2 weighted images and T2 parameter map of the abdomen.

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