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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