Abstract #2713

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

Jonah P. W. Weigand¹, Maria Sedykh², Kai Herz^3,4, Jaume Coll-Font^1,5, Christopher Nguyen^1,5,6, Moritz Zaiss^2,3, 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

Synopsis

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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