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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Vydáno v:JACC. Cardiovascular imaging Ročník 13; číslo 3; s. 684
Hlavní autoři: Ruijsink, Bram, Puyol-Antón, Esther, Oksuz, Ilkay, Sinclair, Matthew, Bai, Wenjia, Schnabel, Julia A, Razavi, Reza, King, Andrew P
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 01.03.2020
Témata:
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
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Shrnutí: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 ventricular function assessment. However, variable image quality, variability in phenotypes of disease, and unavoidable weaknesses in training of DL algorithms currently prevent their use in clinical practice. The framework consists of a pre-analysis DL image QC, followed by a DL algorithm for biventricular segmentation in long-axis and short-axis views, myocardial feature-tracking (FT), and a post-analysis QC to detect erroneous results. The study validated the framework in healthy subjects and cardiac patients by comparison against manual analysis (n = 100) and evaluation of the QC steps' ability to detect erroneous results (n = 700). Next, this method was used to obtain reference values for cardiac function metrics from the UK Biobank. Automated analysis correlated highly with manual analysis for left and right ventricular volumes (all r > 0.95), strain (circumferential r = 0.89, longitudinal r > 0.89), and filling and ejection rates (all r ≥ 0.93). There was no significant bias for cardiac volumes and filling and ejection rates, except for right ventricular end-systolic volume (bias +1.80 ml; p = 0.01). The bias for FT strain was <1.3%. The sensitivity of detection of erroneous output was 95% for volume-derived parameters and 93% for FT strain. Finally, reference values were automatically derived from 2,029 CMR exams in healthy subjects. The study demonstrates a DL-based framework for automated, quality-controlled characterization of cardiac function from cine CMR, without the need for direct clinician oversight.
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ISSN:1876-7591
1876-7591
DOI:10.1016/j.jcmg.2019.05.030