Application of a deep learning image reconstruction (DLIR) algorithm in head CT imaging for children to improve image quality and lesion detection.
Jihang SunHaoyan LiBei WangJianying LiMichelle LiZuofu ZhouYun PengPublished in: BMC medical imaging (2021)
DL-H improves the head CT image quality for children compared with ASIR-V images. The 0.625 mm DL-H images improve lesion detection and produce similar image noise as the 5 mm 50%ASIR-V images, indicating a potential 85% dose reduction if current image quality and slice thickness are desired.
Keyphrases
- image quality
- deep learning
- convolutional neural network
- computed tomography
- artificial intelligence
- dual energy
- young adults
- machine learning
- loop mediated isothermal amplification
- real time pcr
- optical coherence tomography
- high resolution
- label free
- air pollution
- magnetic resonance imaging
- positron emission tomography
- magnetic resonance
- mass spectrometry
- photodynamic therapy
- contrast enhanced
- climate change