Large-scale 3D non-Cartesian coronary MRI reconstruction using distributed memory-efficient physics-guided deep learning with limited training data.
Chi ZhangDavide PicciniOmer Burak DemirelGabriele BonannoChristopher W RoyBurhaneddin YamanSteen MoellerChetan ShenoyMatthias StuberMehmet AkcakayaPublished in: Magma (New York, N.Y.) (2024)
PG-DL reconstruction of large-scale 3D non-Cartesian MRI without compromising image size or network complexity is achieved, and the proposed 2.5D processing enables high-quality reconstruction with limited training data.
Keyphrases
- deep learning
- contrast enhanced
- magnetic resonance imaging
- electronic health record
- big data
- coronary artery disease
- coronary artery
- diffusion weighted imaging
- virtual reality
- artificial intelligence
- machine learning
- computed tomography
- convolutional neural network
- magnetic resonance
- data analysis
- aortic stenosis
- neural network
- ejection fraction