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JACC Cardiovasc Imaging. 2019 Jul 11. pii: S1936-878X(19)30585-6. doi: 10.1016/j.jcmg.2019.05.030. [Epub ahead of print]

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

Author information

1
School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Adult and Paediatric Cardiology, Guy's and St Thomas' NHS Foundation Trust, London, London, United Kingdom. Electronic address: jacobus.ruijsink@kcl.ac.uk.
2
School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
3
Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom; Department of Medicine, Imperial College London, London, United Kingdom.
4
School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Adult and Paediatric Cardiology, Guy's and St Thomas' NHS Foundation Trust, London, London, United Kingdom.

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.

BACKGROUND:

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.

METHODS:

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.

RESULTS:

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.

CONCLUSIONS:

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.

KEYWORDS:

CMR feature tracking; cardiac aging; cardiac function; cardiac magnetic resonance; machine learning; quality control

PMID:
31326477
DOI:
10.1016/j.jcmg.2019.05.030
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