Comparison of machine learning-based CT fractional flow reserve with cardiac MR perfusion mapping for ischemia diagnosis in stable coronary artery disease.
Weifeng GuoShihai ZhaoHaijia XuWei HeLekang YinZhifeng YaoZhihan XuHang JinDong WuChenguang LiShan YangMengsu ZengPublished in: European radiology (2024)
• Both machine learning (ML)-based computed tomography-derived fractional flow reserve (CT-FFR) and quantitative perfusion cardiac MR performed well in the detection of hemodynamically significant stenosis. • Compared with stress myocardial blood flow (MBF) from quantitative perfusion cardiac MR, myocardial perfusion reserve (MPR) provided higher diagnostic performance for detecting hemodynamically significant coronary artery stenosis. • ML-based CT-FFR and MPR from quantitative cardiac MR perfusion yielded similar diagnostic performance in assessing vessel-specific hemodynamically significant stenosis, whereas MPR had a favorable performance in per-patient analysis.
Keyphrases
- contrast enhanced
- computed tomography
- magnetic resonance imaging
- machine learning
- left ventricular
- magnetic resonance
- dual energy
- blood flow
- coronary artery
- coronary artery disease
- high resolution
- positron emission tomography
- image quality
- artificial intelligence
- pulmonary artery
- heart failure
- cardiovascular disease
- percutaneous coronary intervention
- stress induced
- atrial fibrillation
- heat stress
- pulmonary arterial hypertension
- loop mediated isothermal amplification
- transcatheter aortic valve replacement
- ejection fraction
- pet ct