Effect of Deep Learning Image Reconstruction Algorithms on Radiomic Features of Pulmonary Nodules in Ultra-Low-Dose CT.

Journal: Journal of computer assisted tomography

PMID: 39095065

Abstract

OBJECTIVE: The purpose of this study is to explore the impact of deep learning image reconstruction (DLIR) algorithm on the quantification of radiomic features in ultra-low-dose computed tomography (ULD-CT) compared with adaptive statistical iterative reconstruction-Veo (ASIR-V).

Authors

Zhijuan Zheng

Department of Ultrasound, the Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, People's Republic of China.
Yuying Liang

Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
Zhehao Wu
Qijia Han

Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
Zhu Ai

Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, China.
Kun Ma

School of Life Science and Medicine, Dalian University of Technology, F03 Building, No. 2 Dagong Road, Liaodongwan District, Panjin, Liaoning, China.
Zhiming Xiang

Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, 510500, China.

Keywords

Adult Aged Algorithms Deep Learning Female Humans Lung Lung Neoplasms Male Middle Aged Multiple Pulmonary Nodules Radiation Dosage Radiographic Image Interpretation, Computer-Assisted Radiomics Reproducibility of Results Retrospective Studies Solitary Pulmonary Nodule Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (39095065)

Effect of Deep Learning Image Reconstruction Algorithms on Radiomic Features of Pulmonary Nodules in Ultra-Low-Dose CT.

Abstract

Authors

Keywords

External Resources

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