Meeting Banner
Abstract #2517

Learning-based prediction of encoding capability for acquisition schedule of Magnetization Transfer Contrast MR fingerprinting

Beomgu Kang1, Hye-Young Heo2,3, and Hyunwook Park1
1School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea, Republic of, 2Division of MR Research, Department of Radiology, 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 used to quantify multiple tissue parameters of free bulk water and semisolid macromolecule using pseudo-randomized MRF schedules. An optimal design of the MRF schedule is important to improve the quantification accuracy and reduce the scan time. However, lack of the objective function that represents the encoding capability of MRF schedule hinders the reliable optimization. In this study, we propose a novel metric that represents the encoding capability of MRF schedule based on recurrent neural network. Unlike the conventional metrics based on indirect measurements, the proposed learning-based metric directly measures the tissue quantification errors.

This abstract and the presentation materials are available to members only; a login is required.

Join Here