Abstract #0168

Learning-based Optimization of Acquisition Schedule for Magnetization Transfer Contrast MR Fingerprinting

Beomgu Kang¹, Byungjai Kim¹, Hye-Young Heo^2,3, and Hyunwook Park¹

¹Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea, Republic of, ²Russell H Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD, United States, ³F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States

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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