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Abstract #2713

Acceleration of Quantitative Semisolid MT/CEST Imaging using a Generative Adversarial Network (GAN-CEST)

Jonah P. W. Weigand1, Maria Sedykh2, Kai Herz3,4, Jaume Coll-Font1,5, Christopher Nguyen1,5,6, Moritz Zaiss2,3, Christian T. Farrar1, and Or Perlman1
1Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States, 2Department of Neuroradiology, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), University Hospital Erlangen, Erlangen, Germany, 3Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tubingen, Germany, 4Department of Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany, 5Cardiovascular Research Center, Cardiology Division, Massachusetts General Hospital, Charlestown, MA, United States, 6Health Science Technology, Harvard-MIT, Cambridge, MA, United States


Quantitative metabolite concentration and pH biomarker maps, as provided by semisolid MT/CEST-MR-Fingerprinting (MRF), constitute a useful means for determining the molecular origin of pathology. However, the lengthy dictionary generation time and the prolonged 3D acquisition time may hinder clinical dissemination. Here, we developed a generative adversarial network (GAN), aimed to drastically shorten the 3D semisolid MT/CEST-MRF acquisition time and circumvent the need for dictionary generation. In-vitro and in-vivo experiments in 4 volunteers and a patient were conducted at 3 different sites using 3 different scanner models, showing substantial reduction in scan time, while retaining a good agreement with ground-truth reference.

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