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Quantitative cardiac MRI

Seraphim, A; Knott, KD; Augusto, J; Bhuva, AN; Manisty, C; Moon, JC; (2020) Quantitative cardiac MRI. Journal of Magnetic Resonance Imaging , 51 (3) Green open access

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Abstract

Cardiac MRI has become an indispensable imaging modality in the investigation of patients with suspected heart disease. It has emerged as the gold standard test for cardiac function, volumes, and mass and allows noninvasive tissue characterization and the assessment of myocardial perfusion. Quantitative MRI already has a key role in the development and incorporation of machine learning in clinical imaging, potentially offering major improvements in both workflow efficiency and diagnostic accuracy. As the clinical applications of a wide range of quantitative cardiac MRI techniques are being explored and validated, we are expanding our capabilities for earlier detection, monitoring, and risk stratification of disease, potentially guiding personalized management decisions in various cardiac disease models. In this article we review established and emerging quantitative techniques, their clinical applications, highlight novel advances, and appraise their clinical diagnostic potential.

Type: Article
Title: Quantitative cardiac MRI
Location: United States
Open access status: An open access version is available from UCL Discovery
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: machine learning, mapping, perfusion, quantitative, tissue characterization
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science > Clinical Science
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics
URI: https://discovery-pp.ucl.ac.uk/id/eprint/10076525
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