Quantification of fractional flow reserve based on angiographic image data.

12:46 EDT 21st October 2014 | BioPortfolio

Summary of "Quantification of fractional flow reserve based on angiographic image data."

Coronary angiography provides excellent visualization of coronary arteries, but has limitations in assessing the clinical significance of a coronary stenosis. Fractional flow reserve (FFR) has been shown to be reliable in discerning stenoses responsible for inducible ischemia. The purpose of this study is to validate a technique for FFR quantification using angiographic image data. The study was carried out on 10 anesthetized, closed-chest swine using angioplasty balloon catheters to produce partial occlusion. Angiography based FFR was calculated from an angiographically measured ratio of coronary blood flow to arterial lumen volume. Pressure-based FFR was measured from a ratio of distal coronary pressure to aortic pressure. Pressure-wire measurements of FFR (FFR( P )) correlated linearly with angiographic volume-derived measurements of FFR (FFR( V )) according to the equation: FFR( P ) = 0.41 FFR( V ) + 0.52 (P-value < 0.001). The correlation coefficient and standard error of estimate were 0.85 and 0.07, respectively. This is the first study to provide an angiographic method to quantify FFR in swine. Angiographic FFR can potentially provide an assessment of the physiological severity of a coronary stenosis during routine diagnostic cardiac catheterization without a need to cross a stenosis with a pressure-wire.

Affiliation

Department of Radiological Sciences, Medical Sciences I, B-140, University of California, Irvine, CA, 92697, USA.

Journal Details

This article was published in the following journal.

Name: The international journal of cardiovascular imaging
ISSN: 1875-8312
Pages:

Links

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Medical and Biotech [MESH] Definitions

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Signal and data processing method that uses decomposition of wavelets to approximate, estimate, or compress signals with finite time and frequency domains. It represents a signal or data in terms of a fast decaying wavelet series from the original prototype wavelet, called the mother wavelet. This mathematical algorithm has been adopted widely in biomedical disciplines for data and signal processing in noise removal and audio/image compression (e.g., EEG and MRI).

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