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Abstract #1455

In-Vivo Sub-Minute rNOE Mapping Using AutoCEST: a Machine-Learning Approach for CEST/MT Protocol Invention and Quantitative Reconstruction

Or Perlman1, Bo Zhu1,2, Moritz Zaiss3,4, Naoyuki Shono5, Hiroshi Nakashima5, E. Antonio Chiocca5, Matthew S. Rosen1,2, and Christian T. Farrar1
1Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, 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, 5Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, United States

The long acquisition-time and the semi-quantitative nature of the typical CEST-MRI experiment constitute a major obstacle for its clinical adoption. Recently, a machine-learning approach termed AutoCEST was developed, for the automatic design of the optimal acquisition schedule and the reconstruction of quantitative 2-pool CEST maps. Here, we expand this approach for in-vivo scenarios, by incorporating the semisolid-pool into the underlying computational-graph and allowing 3 pools. AutoCEST was evaluated for quantitative rNOE mapping using a GBM mouse model, resulting in a total acquisition and reconstruction times of 49.15s. The tumor rNOE volume-fraction was significantly decreased, in agreement with previous human studies.

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