Background: Quantitative myocardial perfusion imaging is increasingly used for the diagnosis of coronary artery disease. Quantitative perfusion imaging allows to noninvasively calculate fractional flow reserve (FFR). This so-called relative flow reserve (RFR) is defined as the ratio of hyperemic myocardial blood flow (MBF) in a stenotic area to hyperemic MBF in a normal perfused area. The aim of this study was to assess the value of RFR in the detection of significant coronary artery disease.
Methods And Results: From a clinical population of patients with suspected coronary artery disease who underwent oxygen-15-labeled water cardiac positron emission tomography and invasive coronary angiography, 92 patients with single- or 2-vessel disease were included. Intermediate lesions (diameter stenosis, 30%-90%; n=75) were interrogated by FFR. Thirty-eight (41%) vessels were deemed hemodynamically significant (>90% stenosis or FFR≤0.80). Hyperemic MBF, coronary flow reserve, and RFR were lower for vessels with a hemodynamically significant lesion (2.01±0.78 versus 2.90±1.16 mL·min(-1)·g(-1); P<0.001, 2.27±1.03 versus 3.10±1.29; P<0.001, and 0.67±0.23 versus 0.93±0.15; P<0.001, respectively). The correlation between RFR and FFR was moderate (r=0.54; P<0.01). Receiver operator characteristic curve analysis showed an area under the curve of 0.82 for RFR, which was not significantly higher compared with that for hyperemic MBF and coronary flow reserve (0.76; P=0.32 and 0.72; P=0.08, respectively).
Conclusions: Noninvasive estimation of FFR by quantitative perfusion positron emission tomography by calculating RFR is feasible, yet only a trend toward a slight improvement of diagnostic accuracy compared with hyperemic MBF assessment was determined.
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http://dx.doi.org/10.1161/CIRCIMAGING.114.002400 | DOI Listing |
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Department of Internal Medicine and Division of Cardiology, Baylor Scott and White, Temple, TX, United States of America. Electronic address:
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College of Chemistry and Life Science, Beijing University of Technology, Beijing, China.
The accurate non-invasive detection and estimation of central aortic pressure waveforms (CAPW) are crucial for reliable treatments of cardiovascular system diseases. But the accuracy and practicality of current estimation methods need to be improved. Our study combines a meta-learning neural network and a physics-driven method to accurately estimate CAPW based on personalized physiological indicators.
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