MR fingerprinting for semisolid magnetization transfer and chemical exchange saturation transfer quantification

• Published in: NMR in biomedicine Vol. 36; no. 6; pp. e4710 - n/a
• Main Authors: Perlman, Or, Farrar, Christian T., Heo, Hye‐Young
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01.06.2023
• Subjects: Algorithms; Artificial Intelligence; Biological products; CEST; chemical exchange rate; deep learning; Exchanging; Fingerprinting; Image contrast; Image processing; Image Processing, Computer-Assisted; Image reconstruction; Machine learning; Magnetic Resonance Imaging - methods; Magnetization; MR fingerprinting (MRF); Optimization; Protons; Quantitation; quantitative imaging; Semisolids; Unsupervised learning; quantitative imaging; MR fingerprinting (MRF); deep learning; MT; pH; chemical exchange rate; CEST; unsupervised learning
• ISSN: 0952-3480; 1099-1492; 1099-1492
• DOI: 10.1002/nbm.4710

Chemical exchange saturation transfer (CEST) MRI has positioned itself as a promising contrast mechanism, capable of providing molecular information at sufficient resolution and amplified sensitivity. However, it has not yet become a routinely employed clinical technique, due to a variety of confounding factors affecting its contrast‐weighted image interpretation and the inherently long scan time. CEST MR fingerprinting (MRF) is a novel approach for addressing these challenges, allowing simultaneous quantitation of several proton exchange parameters using rapid acquisition schemes. Recently, a number of deep‐learning algorithms have been developed to further boost the performance and speed of CEST and semi‐solid macromolecule magnetization transfer (MT) MRF. This review article describes the fundamental theory behind semisolid MT/CEST‐MRF and its main applications. It then details supervised and unsupervised learning approaches for MRF image reconstruction and describes artificial intelligence (AI)‐based pipelines for protocol optimization. Finally, practical considerations are discussed, and future perspectives are given, accompanied by basic demonstration code and data. Semisolid MT/CEST MR fingerprinting (MRF) is a novel approach for simultaneous quantitation of several proton exchange parameters using rapid acquisition schemes. Recently, several deep‐learning algorithms have been developed to further boost the performance and speed of this imaging strategy. This review article describes the fundamental theory behind semisolid MT/CEST‐MRF, its main applications, deep‐learning approaches for MRF image reconstruction and protocol optimization, accompanied by basic demonstration code and data.