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