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Abstract #0103

MR-double-zero - Can a machine discover new MRI contrasts, such as metabolite concentration?

Sebastian Mueller1,2, Felix Glang1, Kai Herz1,2, Klaus Scheffler1,2, and Moritz Zaiss1,3
1High-field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, 2Department of Biomedical Magnetic Resonance, Eberhard Karls University Tübingen, Tuebingen, Germany, 3Department of Neuroradiology, University Hospital Erlangen, Erlangen, Germany


Discovery of MR contrast and/or conventional sequence parameter optimization usually requires a theoretical model to describe MR physics. Here we investigate if novel contrasts can be found by directly running numerical optimization on a real MRI scanner instead of a simulation. To this end, a derivative-free optimization algorithm is set up to repeatedly update and execute a parametrized sequence on the scanner and map the acquired signals to a given target contrast. As proof-of-principle, we show that this enables creatine concentration mapping by learning a CEST-prepared sequence, which is found solely based on known target concentrations in a phantom.

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