Motion-corrected coronary calcium scores by a convolutional neural network: a robotic simulating study.
Yaping ZhangNiels R van der WerfBeibei JiangRobbert van HamersveltMarcel J W GreuterXueqian XiePublished in: European radiology (2019)
• A deep CNN architecture trained by CT images of motion artifacts showed the ability to correct coronary calcium scores from blurred images. • A correction algorithm based on deep CNN can be used for a tenfold reduction in Agatston score variations from 38 to 3.7% of moving coronary calcified plaques and to improve the sensitivity from 65 to 85% for the detection of calcifications. • This experimental study provides a method to improve its accuracy for coronary calcium scores that is a fundamental step towards a real clinical scenario.
Keyphrases
- convolutional neural network
- deep learning
- coronary artery disease
- coronary artery
- aortic stenosis
- computed tomography
- machine learning
- magnetic resonance imaging
- optical coherence tomography
- minimally invasive
- high speed
- transcatheter aortic valve replacement
- heart failure
- left ventricular
- positron emission tomography
- mass spectrometry
- aortic valve
- atrial fibrillation