Denoising of pediatric low dose abdominal CT using deep learning based algorithm.

Journal: PloS one

Published Date: Jan 1, 2022

Abstract

OBJECTIVES: To evaluate standard dose-like computed tomography (CT) images generated by a deep learning method, trained using unpaired low-dose CT (LDCT) and standard-dose CT (SDCT) images.

Authors

Hyoung Suk Park

Division of Integrated Mathematics, National Institute for Mathematical Sciences, Daejeon, 34047, Korea.
Kiwan Jeon
Jeongeun Lee

Interdisciplinary Program for Bioengineering, Graduate School, Seoul National Universty, Seoul, Republic of Korea.
Sun Kyoung You

Department of Radiology, Chungnam National University College of Medicine, Daejeon, Republic of Korea.

Keywords

Abdomen Algorithms Child Child, Preschool Deep Learning Female Humans Image Processing, Computer-Assisted Male Radiation Dosage Radiography, Abdominal Signal-To-Noise Ratio Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (35061701)

Denoising of pediatric low dose abdominal CT using deep learning based algorithm.

Abstract

Authors

Keywords

External Resources

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