eprintid: 10076525
rev_number: 33
eprint_status: archive
userid: 608
dir: disk0/10/07/65/25
datestamp: 2019-06-28 11:26:12
lastmod: 2021-12-14 23:22:36
status_changed: 2019-06-28 11:26:12
type: article
metadata_visibility: show
creators_name: Seraphim, A
creators_name: Knott, KD
creators_name: Augusto, J
creators_name: Bhuva, AN
creators_name: Manisty, C
creators_name: Moon, JC
title: Quantitative cardiac MRI
ispublished: pub
divisions: UCL
divisions: B02
divisions: D14
divisions: GA4
divisions: DD4
keywords: machine learning, mapping, perfusion, quantitative, tissue characterization
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
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.
date: 2020-03
date_type: published
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1661231
lyricists_name: Bhuva, Anish
lyricists_name: Bicho Augusto, João
lyricists_name: Knott, Kristopher
lyricists_name: Manisty, Charlotte
lyricists_name: Moon, James
lyricists_name: Seraphim, Andreas
lyricists_id: ABHUV56
lyricists_id: JABAU89
lyricists_id: KDKNO05
lyricists_id: HMANI16
lyricists_id: JMOON31
lyricists_id: ASERA03
actors_name: Austen, Jennifer
actors_id: JAUST66
actors_role: owner
full_text_status: public
publication: Journal of Magnetic Resonance Imaging
volume: 51
number: 3
event_location: United States
issn: 1522-2586
citation:        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   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10076525/3/Seraphim%20Manuscript_Quantitative%20CMR_%20clean%20version_.pdf