Deep learning image reconstruction for improving image quality of contrast-enhanced dual-energy CT in abdomen.

Mineka SatoYasutaka IchikawaKensuke DomaeKazuya YoshikawaYoshinori KaniiAkio YamazakiNaoki NagasawaMotonori NagataMasaki IshidaHajime Sakuma

Published in: European radiology (2022)

• Deep learning image reconstruction (DLIR) is useful for reducing image noise and improving the CNR of visual monochromatic 40-, 50-, and 70-keV images in dual-energy CT. • DLIR can improve lesion conspicuity of abdominal solid lesions on virtual monochromatic images compared to hybrid iterative reconstruction. • DLIR can also be applied to iodine density maps and significantly improves their image quality.

Keyphrases

dual energy
deep learning
image quality
contrast enhanced
computed tomography
convolutional neural network
artificial intelligence
diffusion weighted
magnetic resonance imaging
machine learning
magnetic resonance
positron emission tomography
optical coherence tomography