Reducing Gadolinium Contrast With Artificial Intelligence.

Autor: Tsui B; Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA., Calabrese E; Department of Radiology, Duke University School of Medicine, Durham, North Carolina, USA., Zaharchuk G; Department of Radiology, Stanford University, Stanford, California, USA., Rauschecker AM; Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California, USA.
Jazyk: angličtina
Zdroj: Journal of magnetic resonance imaging : JMRI [J Magn Reson Imaging] 2024 Sep; Vol. 60 (3), pp. 848-859. Date of Electronic Publication: 2023 Oct 31.
DOI: 10.1002/jmri.29095
Abstrakt: Gadolinium contrast is an important agent in magnetic resonance imaging (MRI), particularly in neuroimaging where it can help identify blood-brain barrier breakdown from an inflammatory, infectious, or neoplastic process. However, gadolinium contrast has several drawbacks, including nephrogenic systemic fibrosis, gadolinium deposition in the brain and bones, and allergic-like reactions. As computer hardware and technology continues to evolve, machine learning has become a possible solution for eliminating or reducing the dose of gadolinium contrast. This review summarizes the clinical uses of gadolinium contrast, the risks of gadolinium contrast, and state-of-the-art machine learning methods that have been applied to reduce or eliminate gadolinium contrast administration, as well as their current limitations, with a focus on neuroimaging applications. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 1.
(© 2023 International Society for Magnetic Resonance in Medicine.)
Databáze: MEDLINE
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