Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT.
Motonori AkagiYuko NakamuraToru HigakiKeigo NaritaYukiko HondaJian ZhouZhou YuNaruomi AkinoKazuo AwaiPublished in: European radiology (2019)
• The potential degradation due to increased noise may prevent implementation of ultra-high-resolution CT in the abdomen. • Image noise and overall image quality for hepatic ultra-high-resolution CT images improved with deep learning reconstruction as compared to hybrid- and model-based iterative reconstruction.
Keyphrases
- image quality
- high resolution
- deep learning
- computed tomography
- dual energy
- convolutional neural network
- artificial intelligence
- mass spectrometry
- air pollution
- machine learning
- primary care
- tandem mass spectrometry
- healthcare
- high speed
- magnetic resonance imaging
- quality improvement
- magnetic resonance
- climate change
- human health
- contrast enhanced