Automated quantification of myocardial tissue characteristics from native T mapping using neural networks with uncertainty-based quality-control.

Journal: Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance

Published Date: Aug 20, 2020

Abstract

BACKGROUND: Tissue characterisation with cardiovascular magnetic resonance (CMR) parametric mapping has the potential to detect and quantify both focal and diffuse alterations in myocardial structure not assessable by late gadolinium enhancement. Native T mapping in particular has shown promise as a useful biomarker to support diagnostic, therapeutic and prognostic decision-making in ischaemic and non-ischaemic cardiomyopathies.

Authors

Esther Puyol-Anton
Bram Ruijsink
Christian F Baumgartner
Pier-Giorgio Masci

School of Biomedical Engineering & Imaging Sciences, King's College London, Rayne Institute, 4th Floor Lambeth Wing St Thomas Hospital, Westminster Bridge Road, London, SE1 7EH, UK.
Matthew Sinclair

Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom.
Ender Konukoglu
Reza Razavi
Andrew P King

Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom. Electronic address: andrew.king@kcl.ac.uk.

Keywords

Automation Bayes Theorem Cardiomyopathies Case-Control Studies Humans Image Interpretation, Computer-Assisted Magnetic Resonance Imaging Myocardium Neural Networks, Computer Predictive Value of Tests Quality Control Reproducibility of Results Stroke Volume Uncertainty Ventricular Function, Left

External Resources

View on PubMed Access via DOI PubMed (32814579)

Automated quantification of myocardial tissue characteristics from native T mapping using neural networks with uncertainty-based quality-control.

Abstract

Authors

Keywords

External Resources

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