JACC: Cardiovascular Imaging
Quantitative Myocardial Perfusion Imaging Versus Visual Analysis in Diagnosing Myocardial IschemiaA CE-MARC Substudy
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Author + information
- John D. Biglands, BSc, MSc, PhDa,b,∗ (j.biglands{at}nhs.net),
- Montasir Ibraheem, MSca,
- Derek R. Magee, BSc, PhDc,
- Aleksandra Radjenovic, BSc, MSc, PhDd,
- Sven Plein, MD, PhDa and
- John P. Greenwood, MBChB, PhDa
- aDivision of Biomedical Imaging, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom
- bDepartment of Medical Physics and Engineering, Leeds Teaching Hospitals NHS Trust, Leeds, United Kingdom
- cSchool of Computing, University of Leeds, Leeds, United Kingdom
- dInstitute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom
- ↵∗Address for correspondence:
Dr. John D. Biglands, Room 8.6, Division of Medical Physics, Worsley Building, University of Leeds, Leeds, West Yorkshire LS2 9JT, United Kingdom.
Graphical abstract
Abstract
Objectives This study sought to compare the diagnostic accuracy of visual and quantitative analyses of myocardial perfusion cardiovascular magnetic resonance against a reference standard of quantitative coronary angiography.
Background Visual analysis of perfusion cardiovascular magnetic resonance studies for assessing myocardial perfusion has been shown to have high diagnostic accuracy for coronary artery disease. However, only a few small studies have assessed the diagnostic accuracy of quantitative myocardial perfusion.
Methods This retrospective study included 128 patients randomly selected from the CE-MARC (Clinical Evaluation of Magnetic Resonance Imaging in Coronary Heart Disease) study population such that the distribution of risk factors and disease status was proportionate to the full population. Visual analysis results of cardiovascular magnetic resonance perfusion images, by consensus of 2 expert readers, were taken from the original study reports. Quantitative myocardial blood flow estimates were obtained using Fermi-constrained deconvolution. The reference standard for myocardial ischemia was a quantitative coronary x-ray angiogram stenosis severity of ≥70% diameter in any coronary artery of >2 mm diameter, or ≥50% in the left main stem. Diagnostic performance was calculated using receiver-operating characteristic curve analysis.
Results The area under the curve for visual analysis was 0.88 (95% confidence interval: 0.81 to 0.95) with a sensitivity of 81.0% (95% confidence interval: 69.1% to 92.8%) and specificity of 86.0% (95% confidence interval: 78.7% to 93.4%). For quantitative stress myocardial blood flow the area under the curve was 0.89 (95% confidence interval: 0.83 to 0.96) with a sensitivity of 87.5% (95% confidence interval: 77.3% to 97.7%) and specificity of 84.5% (95% confidence interval: 76.8% to 92.3%). There was no statistically significant difference between the diagnostic performance of quantitative and visual analyses (p = 0.72). Incorporating rest myocardial blood flow values to generate a myocardial perfusion reserve did not significantly increase the quantitative analysis area under the curve (p = 0.79).
Conclusions Quantitative perfusion has a high diagnostic accuracy for detecting coronary artery disease but is not superior to visual analysis. The incorporation of rest perfusion imaging does not improve diagnostic accuracy in quantitative perfusion analysis.
- cardiovascular magnetic resonance
- diagnostic accuracy
- myocardial ischemia
- quantitative myocardial perfusion
Footnotes
This study is independent research supported in part by the National Institute for Health Research (NIHR). The views expressed in this publication are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health. Dr. Biglands has received funding from NIHR fellowships (NIHR/RTF/01/08/014, and ICA-CL-2016-02-017). Dr. Magee is partially supported by WELMEC, a Centre of Excellence in Medical Engineering funded by the Wellcome Trust and Engineering and Physical Sciences Research Council (grant WT 088908/Z/09/Z). Dr. Radjenovic is partially supported by WELMEC, a Centre of Excellence in Medical Engineering funded by the Wellcome Trust and Engineering and Physical Sciences Research Council (grant WT 088908/Z/09/Z). Dr. Plein is funded by a British Heart Foundation fellowship (FS/10/62/28409). All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
- Received February 23, 2017.
- Revision received January 26, 2018.
- Accepted February 22, 2018.
- 2018 American College of Cardiology Foundation
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