Fully Automated, Quality-Controlled Cardiac Analysis From CMR: Validation and Large-Scale Application to Characterize Cardiac Function.

Journal: JACC. Cardiovascular imaging

Published Date: Mar 1, 2020

Abstract

OBJECTIVES: This study sought to develop a fully automated framework for cardiac function analysis from cardiac magnetic resonance (CMR), including comprehensive quality control (QC) algorithms to detect erroneous output.

Authors

Bram Ruijsink
Esther Puyol-Anton
İlkay Öksüz

İstanbul Technical University Faculty of Engineering, Department of Computer Engineering, İstanbul, Türkiye.
Matthew Sinclair

Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom.
Wenjia Bai

Department of Computing Imperial College London London UK.
Julia A Schnabel

Division of Imaging Sciences and Biomedical Engineering, King's College London, UK.
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

Aged Automation Deep Learning Diagnosis, Computer-Assisted Female Heart Diseases Humans Image Interpretation, Computer-Assisted Magnetic Resonance Imaging, Cine Male Middle Aged Predictive Value of Tests Quality Control Quality Indicators, Health Care Reproducibility of Results Stroke Volume Ventricular Function, Left Ventricular Function, Right

External Resources

View on PubMed Access via DOI PubMed (31326477)

Fully Automated, Quality-Controlled Cardiac Analysis From CMR: Validation and Large-Scale Application to Characterize Cardiac Function.

Abstract

Authors

Keywords

External Resources

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