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

Or Perlman*, Christian T. Farrar, Hye Young Heo*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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 languageEnglish
Article numbere4710
JournalNMR in Biomedicine
Volume36
Issue number6
DOIs
StatePublished - Jun 2023
Externally publishedYes

Keywords

  • CEST
  • MR fingerprinting (MRF)
  • MT
  • chemical exchange rate
  • deep learning
  • pH
  • quantitative imaging
  • unsupervised learning

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