Feasibility of accelerated non-contrast-enhanced whole-heart bSSFP coronary MR angiography by deep learning-constrained compressed sensing.
Xi WuLu TangWanjiang LiShuai HeXun YuePengfei PengTao WuXiaoyong ZhangZhigang WuYong HeYucheng ChenJuan HuangJiayu SunPublished in: European radiology (2023)
• This prospective study showed that CSAI enables a reduction in acquisition time by 22% with superior diagnostic image quality compared with the SENSE protocol. • CSAI replaces the wavelet transform with a CNN as a sparsifying transform in the CS algorithm, achieving high coronary MR image quality with reduced noise. • CSAI achieved per-patient sensitivity of 87.5% (7/8) and specificity of 91.7% (11/12) respectively for detecting significant coronary stenosis.
Keyphrases
- image quality
- contrast enhanced
- computed tomography
- dual energy
- deep learning
- coronary artery disease
- diffusion weighted
- coronary artery
- magnetic resonance imaging
- convolutional neural network
- magnetic resonance
- diffusion weighted imaging
- randomized controlled trial
- machine learning
- aortic stenosis
- optical coherence tomography
- atrial fibrillation
- heart failure
- artificial intelligence
- ejection fraction
- left ventricular
- transcatheter aortic valve replacement