Coronary flow reserve estimated by positron emission tomography to diagnose significant coronary artery disease and predict cardiac events

Masanao Naya, Nagara Tamaki, Hiroyuki Tsutsui

Research output: Contribution to journalReview articlepeer-review

25 Citations (Scopus)

Abstract

Coronary artery disease (CAD) is a major cause of death in Japan. Coronary angiography is useful to assess the atherosclerotic burden in CAD patients, but its ability to predict whether patients will respond favorably to optimal medical therapy and revascularization is limited. The measurement of the fractional flow reserve with angiography is a well-validated method for identifying ischemic vessels. However, neither an anatomical assessment nor a functional assessment can delineate microvasculature or estimate its function. The quantitative coronary flow reserve (CFR) estimated from sequential myocardial perfusion images obtained by positron emission tomography (PET) during stress provides an accurate index of hyperemic reactivity to vasodilatory agents in the myocardium. In fact, there is growing evidence that the CFR reflects disease activity in the entire coronary circulation, including epicardial coronary artery stenosis, diffuse atherosclerosis, and microvascular dilatory function. Importantly, reduced CFR is observed even in patients without flow-limiting coronary stenosis, and its evaluation can improve the risk stratification of patients at any stage of CAD. This review focuses on the application of CFR estimated by cardiac PET for the diagnosis and risk stratification of patients with CAD.

Original languageEnglish
Pages (from-to)15-23
Number of pages9
JournalCirculation Journal
Volume79
Issue number1
DOIs
Publication statusPublished - Dec 19 2014
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Cardiology and Cardiovascular Medicine

Fingerprint

Dive into the research topics of 'Coronary flow reserve estimated by positron emission tomography to diagnose significant coronary artery disease and predict cardiac events'. Together they form a unique fingerprint.

Cite this