Abstract
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.
Original language | English (US) |
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Article number | e4710 |
Journal | NMR in biomedicine |
Volume | 36 |
Issue number | 6 |
DOIs | |
State | Published - Jun 2023 |
Keywords
- CEST
- MR fingerprinting (MRF)
- MT
- chemical exchange rate
- deep learning
- pH
- quantitative imaging
- unsupervised learning
ASJC Scopus subject areas
- Molecular Medicine
- Radiology Nuclear Medicine and imaging
- Spectroscopy