Deep learning image reconstruction algorithm reduces image noise while alters radiomics features in...
Deep learning image reconstruction algorithm reduces image noise while alters radiomics features in dual-energy CT in comparison with conventional iterative reconstruction algorithms: a phantom study
About this item
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Author / Creator
Zhong, Jingyu , Xia, Yihan , Chen, Yong , Li, Jianying , Lu, Wei , Shi, Xiaomeng , Feng, Jianxing , Yan, Fuhua , Yao, Weiwu and Zhang, Huan
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Journal title
Language
English
Formats
Publication information
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Subjects
More information
Scope and Contents
Contents
Objectives
To compare image quality between a deep learning image reconstruction (DLIR) algorithm and conventional iterative reconstruction (IR) algorithms in dual-energy CT (DECT) and to assess the impact of these algorithms on radiomics robustness.
Methods
A phantom with clinical-relevant densities was imaged on seven DECT scanners with...
Alternative Titles
Full title
Deep learning image reconstruction algorithm reduces image noise while alters radiomics features in dual-energy CT in comparison with conventional iterative reconstruction algorithms: a phantom study
Authors, Artists and Contributors
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_proquest_miscellaneous_2721639942
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_miscellaneous_2721639942
Other Identifiers
ISSN
1432-1084,0938-7994
E-ISSN
1432-1084
DOI
10.1007/s00330-022-09119-1
