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Abstract #3098

AutoCEST: a Machine-Learning Approach for Optimal CEST-MRI Experiment Design and Quantitative Mapping

Or Perlman1, Bo Zhu1,2, Moritz Zaiss3,4, Matthew S. Rosen1,2, and Christian T. Farrar1
1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States, 2Department of Physics, Harvard University, Cambridge, MA, United States, 3Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany, 4Department of Neuroradiology, University Clinic Erlangen, Erlangen, Germany

The most common metric for CEST analysis is the magnetization-transfer-ratio asymmetry. Although qualitatively useful, it is affected by a mixed contribution from several exchange properties and requires experiment-specific protocol optimization. Herein, we propose a machine-learning framework for simultaneously tackling two challenging tasks: (1) automatic design of the optimal CEST acquisition schedule; (2) automatic extraction of fully quantitative CEST maps from the acquired data. The method was evaluated in simulations and phantoms at 4.7T. The resulting data acquisition and reconstruction times were 52 s and 36 ms respectively, providing quantitative exchange-rate and volume fraction maps with good agreement to ground-truth.

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