Learning‐based optimization of acquisition schedule for magnetization transfer contrast MR fingerprinting. (22nd December 2021)

• Record Type: Journal Article
• Authors: Kang, Beomgu; Kim, Byungjai; Park, HyunWook; Heo, Hye‐Young
• Is Part Of: NMR in biomedicine. Volume 35:Number 5(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-22
• Subjects: CEST -- deep learning -- MR fingerprinting -- MT -- NOE -- optimization; Nuclear magnetic resonance -- Periodicals; Magnetic Resonance Spectroscopy -- Periodicals; 574
• DOI: 10.1002/nbm.4662
• Languages: English
• ISSNs: 0952-3480

Abstract

Abstract: 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. To improve acquisition efficiency and reconstruction accuracy, the optimization of MRF sequence design has been of recent interest in the MRF field, but has been challenging due to the large number of degrees of freedom to be optimized in the sequence. Herein, we propose a framework for learning‐based optimization of the acquisition schedule (LOAS), which optimizes RF saturation‐encoded MRF acquisitions with a minimal number of scan parameters for tissue parameter determination. In a supervised learning framework, scan parameters were subsequently updated to minimize a predefined loss function that can directly represent tissue quantification errors. We evaluated the performance of the proposed approach with a numerical phantom and in in vivo experiments. For validation, MRF images were synthesized using the tissue parameters estimated from a fully connected neural network framework and compared with references. Our results showed that LOAS outperformed existing indirect optimization methods with regard to quantification accuracy and acquisition efficiency. The proposed LOAS method could be a powerful optimization tool in the design of MRF pulse sequences. Abstract : We proposed a learning‐based optimization framework to accelerate data acquisition and improve quantification accuracy for magnetization transfer contrast MR fingerprinting. Unlike optimization methods based on indirect measurements, the proposed approach optimized scan parameters by directly calculating quantitative errors of the tissue parameters, significantly improved the accuracy, and reduced the data acquisition time. The flexible LOAS framework could be a powerful optimization tool for MRF pulse sequence design.