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

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. Analysis of cine CMR imaging using deep learning (DL) algorithms could automate ventricula...

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Veröffentlicht in:	JACC. Cardiovascular imaging Jg. 13; H. 3; S. 684
Hauptverfasser:	Ruijsink, Bram, Puyol-Antón, Esther, Oksuz, Ilkay, Sinclair, Matthew, Bai, Wenjia, Schnabel, Julia A, Razavi, Reza, King, Andrew P
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States 01.03.2020
Schlagworte:	Aged Automation Deep Learning - standards Diagnosis, Computer-Assisted - standards Female Heart Diseases - diagnostic imaging Heart Diseases - physiopathology Humans Image Interpretation, Computer-Assisted - standards Magnetic Resonance Imaging, Cine - standards Male Middle Aged Predictive Value of Tests Quality Control Quality Indicators, Health Care - standards Reproducibility of Results Stroke Volume Ventricular Function, Left Ventricular Function, Right cardiac function CMR feature tracking quality control machine learning cardiac magnetic resonance cardiac aging
ISSN:	1876-7591, 1876-7591
Online-Zugang:	Weitere Angaben
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