MR fingerprinting for semisolid magnetization transfer and chemical exchange saturation transfer quantification.
NMR Biomed
; 36(6): e4710, 2023 06.
Article
em En
| MEDLINE
| ID: mdl-35141967
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.
Palavras-chave
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Inteligência Artificial
/
Imageamento por Ressonância Magnética
Tipo de estudo:
Guideline
Idioma:
En
Revista:
NMR Biomed
Assunto da revista:
DIAGNOSTICO POR IMAGEM
/
MEDICINA NUCLEAR
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Estados Unidos