Automated inversion time selection for late gadolinium-enhanced cardiac magnetic resonance imaging.
Cheng XieRory ZhangSebastian MensinkRahul GandharvaMustafa AwniHester LimStefan E KachelErnest CheungRichard CrawleyLeonid ChurilovNuno BettencourtAmedeo ChiribiriCian M ScannellRuth P LimPublished in: European radiology (2024)
• A model comprising convolutional and recurrent neural networks was developed to extract optimal TI from TI scout images. • Model accuracy within 50 ms of ground truth on multi-vendor holdout and external data of 96.1% and 97.3% respectively was achieved. • This model could improve workflow efficiency and standardise optimal TI selection for consistent LGE imaging.
Keyphrases
- neural network
- magnetic resonance imaging
- deep learning
- contrast enhanced
- machine learning
- high resolution
- computed tomography
- mass spectrometry
- oxidative stress
- heart failure
- high throughput
- left ventricular
- convolutional neural network
- optical coherence tomography
- magnetic resonance
- photodynamic therapy
- fluorescence imaging