医学
灌注
第一次通过
心脏病学
放射科
内科学
数学
算术
作者
Roberta Catania,Sandra Quinn,Amir Ali Rahsepar,Tugce Agirlar Trabzonlu,Jay B. Bisen,Kelvin Chow,Daniel Lee,Ryan Avery,Peter Kellman,Bradley D. Allen
出处
期刊:Radiographics
[Radiological Society of North America]
日期:2025-02-20
卷期号:45 (3)
被引量:1
摘要
Quantitative stress perfusion (qPerf) cardiac magnetic resonance (CMR) imaging is a noninvasive approach used to quantify myocardial blood flow (MBF). Compared with visual analysis, qPerf CMR has superior diagnostic accuracy in the detection of myocardial ischemia and assessment of ischemic burden. In the evaluation of epicardial coronary artery disease (CAD), qPerf CMR improves the distinction of single-vessel from multivessel disease, yielding a more accurate estimate of the ischemic burden, and in turn improving patient management. In patients with chest pain without epicardial CAD, the findings of lower stress MBF and myocardial perfusion reserve (MPR) allow the diagnosis of microvascular dysfunction (MVD). Given its accuracy, MBF quantification with stress CMR has been introduced into the most recent recommendations for diagnosis in patients who have ischemia with nonobstructive CAD. Recent studies have shown a greater decrease in stress MBF and MPR in patients with three-vessel CAD compared with those in patients with MVD, demonstrating an important role that quantitative stress CMR can play in differentiating these etiologies in patients with stable angina. In cases of hypertrophic cardiomyopathy and cardiac amyloidosis, qPerf CMR aids in early diagnosis of ischemia and in risk assessment. Ischemia also results from alterations in hemodynamics that may occur with valve disease such as aortic stenosis or in cases of heart failure. qPerf CMR has emerged as a useful noninvasive tool for detection of cardiac allograft vasculopathy in patients who have undergone heart transplant. The authors review the basic principles and current primary clinical applications of qPerf CMR. ©RSNA, 2025 Supplemental material is available for this article. See the invited commentary by Leung and Ng in this issue.
科研通智能强力驱动
Strongly Powered by AbleSci AI