High-strength deep learning image reconstruction in coronary CT angiography at 70-kVp tube voltage significantly improves image quality and reduces both radiation and contrast doses.
Wanjiang LiKaiyue DiaoYuting WenTao ShuaiYongchun YouJin ZhaoKai LiaoChunyan LuJianqun YuYong HeZhen-Lin LiPublished in: European radiology (2022)
) patients acquired at sub-mSv radiation dose and 24 mL contrast dose through the combination of 70-kVp tube voltage and DLIR-H algorithm achieves excellent diagnostic image quality with a good inter-rater agreement. • DLIR-H algorithm shows a higher capacity of significantly reducing image noise than adaptive statistical iterative reconstruction algorithm in CCTA examination.
Keyphrases
- image quality
- deep learning
- computed tomography
- machine learning
- artificial intelligence
- convolutional neural network
- dual energy
- end stage renal disease
- magnetic resonance
- ejection fraction
- newly diagnosed
- chronic kidney disease
- coronary artery
- coronary artery disease
- peritoneal dialysis
- prognostic factors
- radiation therapy
- air pollution
- aortic stenosis
- neural network