Fully automated, quality-controlled cardiac analysis from CMR: validation and large-scale application to characterize cardiac function

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Abstract

Objectives:Develop a fully automated framework for cardiac function analysis from cardiacmagnetic resonance (CMR), including comprehensive quality control (QC) algorithmsto detect erroneous output.Background:Analysis of cine CMR imaging using deep learning (DL) algorithms could automateventricular function assessment. However, variable image quality, variability inphenotypes of disease and unavoidable weaknesses in training of DL algorithmscurrently prevent their use in clinical practice.Methods:The framework consists of a pre-analysis DL image QC, followed by a DL algorithmfor biventricular segmentation in long- and short-axis, myocardial feature-tracking(FT) and a post-analysis QC to detect erroneous results. We validated the frameworkin 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, we utilized our method to obtain reference values for cardiac function metricsfrom the UK Biobank.Results:Automated analysis correlated highly with manual analysis for left and rightventricular 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 cardiacvolumes and filling and ejection rates, except for RVESV (bias +1.80 mL, p=.01).The bias for FT-strain was <1.3%. The sensitivity of detection of erroneous outputwas 95% for volume-derived parameters and 93% for FT strain. Finally, referencevalues were automatically derived from 2,029 CMR exams in healthy subjects.Conclusions:We demonstrate a DL-based framework for automated, quality-controlledcharacterization of cardiac function from cine CMR, without the need for directclinician oversight.
Original languageEnglish
Pages (from-to)684-695
JournalJACC Cardiovascular Imaging
Volume13
Issue number3
Early online date17 Jul 2019
DOIs
Publication statusPublished - 2019

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