Feasibility of Deep Learning-Based Noise and Artifact Reduction in Coronal Reformation of Contrast-Enhanced Chest Computed Tomography.

Journal: Journal of computer assisted tomography

Published Date: May 20, 2022

Abstract

PURPOSE: This study aimed to evaluate the feasibility of a deep learning method for imaging artifact and noise reduction in coronal reformation of contrast-enhanced chest computed tomography (CT).

Authors

Eun-Ju Kang

From the Department of Radiology, College of Medicine, Dong-A University, Busan.
Hyoung Suk Park

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

Department of Radiology, Pusan National University Hospital, Pusan National University School of Medicine and Medical Research Institute, Busan.
Jae-Kwang Lim

Department of Radiology, School of Medicine, Kyungpook National University, Daegu, Korea.

Keywords

Algorithms Artifacts Deep Learning Feasibility Studies Humans Image Processing, Computer-Assisted Radiographic Image Interpretation, Computer-Assisted Signal-To-Noise Ratio Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (35617647)

Feasibility of Deep Learning-Based Noise and Artifact Reduction in Coronal Reformation of Contrast-Enhanced Chest Computed Tomography.

Abstract

Authors

Keywords

External Resources

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