The influence of image quality on diagnostic performance of a machine learning-based fractional flow reserve derived from coronary CT angiography.
Peng Peng XuJian Hua LiFan ZhouMeng Di JiangChang Sheng ZhouMeng Jie LuChun Xiang TangXiao Lei ZhangLiu YangYuan Xiu ZhangYi Ning WangJia Yin ZhangMeng Meng YuYang HouMin Wen ZhengBo ZhangDai Min ZhangYan YiLei XuXiu Hua HuHui LiuGuang Ming LuQian Qian NiLong Jiang ZhangPublished in: European radiology (2020)
• FFRCTcan be used to evaluate lesion-specific ischemia. • Poor image quality negatively affects the diagnostic performance of FFRCT. • CCTA with ≥ score 3, intracoronary enhancement degree of 300-400 HU, and heart rate below 70 bpm at scanning could be of great benefit to more accurate FFRCTanalysis.
Keyphrases
- image quality
- heart rate
- machine learning
- heart rate variability
- computed tomography
- blood pressure
- high resolution
- coronary artery disease
- coronary artery
- dual energy
- artificial intelligence
- electron microscopy
- big data
- magnetic resonance imaging
- st elevation myocardial infarction
- deep learning
- magnetic resonance
- acute coronary syndrome
- aortic valve