# Only-Train-Once MR Fingerprinting for Magnetization Transfer Contrast Quantification Source: [Only-Train-Once MR Fingerprinting for Magnetization Transfer Contrast Quantification]() Collection index: [llms.txt]() Data updated: 2026-01-02T12:53:07.732Z Authors: Beomgu Kang, Hye-Young Heo, HyunWook Park Published: 2022-06-09T09:51:39.000Z ## Abstract Magnetization transfer contrast magnetic resonance fingerprinting (MTC-MRF) is a novel quantitative imaging technique that simultaneously measures several tissue parameters of semisolid macromolecule and free bulk water. In this study, we propose an Only-Train-Once MR fingerprinting (OTOM) framework that estimates the free bulk water and MTC tissue parameters from MR fingerprints regardless of MRF schedule, thereby avoiding time-consuming process such as generation of training dataset and network training according to each MRF schedule. A recurrent neural network is designed to cope with two types of variants of MRF schedules: 1) various lengths and 2) various patterns. Experiments on digital phantoms and in vivo data demonstrate that our approach can achieve accurate quantification for the water and MTC parameters with multiple MRF schedules. Moreover, the proposed method is in excellent agreement with the conventional deep learning and fitting methods. The flexible OTOM framework could be an efficient tissue quantification tool for various MRF protocols. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Tissue parameter estimation on Bloch-McConnell equation-based digital phantoms](): Tissue parameter estimation; Bloch-McConnell equation-based digital phantoms; MAE (kmw): 10.29. ## Original sources and implementation [Original paper]() [PDF]()