Magnetization transfer contrast MR fingerprinting (MTC-MRF) is a novel quantitative imaging method that simultaneously quantifies free bulk water and semisolid macromolecule parameters using pseudo-randomized scan parameters. Here, we propose a framework for learning-based optimization of the acquisition schedule (LOAS), which optimizes RF saturation-encoded MRF acquisitions with a minimum number of acquisitions for tissue parameter estimation. Unlike the optimization methods based on indirect measurements, the proposed approach can optimize scan parameters by directly computing quantitative errors in tissue parameters.
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