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Impact of a deep learning image reconstruction algorithm on the robustness of abdominal computed tomography radiomics features using standard and low radiation doses.

作者信息

Yang Shuo, Bie Yifan, Zhao Lei, Luan Kun, Li Xingchao, Chi Yanheng, Bian Zhen, Zhang Deqing, Pang Guodong, Zhong Hai

机构信息

Department of Radiology, the Second Hospital of Shandong University, Jinan, China.

出版信息

Quant Imaging Med Surg. 2025 Sep 1;15(9):7922-7934. doi: 10.21037/qims-2025-238. Epub 2025 Aug 18.


DOI:10.21037/qims-2025-238
PMID:40893527
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12397659/
Abstract

BACKGROUND: Deep learning image reconstruction (DLIR) can enhance image quality and lower image dose, yet its impact on radiomics features (RFs) remains unclear. This study aimed to compare the effects of DLIR and conventional adaptive statistical iterative reconstruction-Veo (ASIR-V) algorithms on the robustness of RFs using standard and low-dose abdominal clinical computed tomography (CT) scans. METHODS: A total of 54 patients with hepatic masses who underwent abdominal contrast-enhanced CT scans were retrospectively analyzed. The raw data of standard dose in the venous phase and low dose in the delayed phase were reconstructed using five reconstruction settings, including ASIR-V at 30% (ASIR-V30%) and 70% (ASIR-V70%) levels, and DLIR at low (DLIR-L), medium (DLIR-M), and high (DLIR-H) levels. The PyRadiomics platform was used for the extraction of RFs in 18 regions of interest (ROIs) in different organs or tissues. The consistency of RFs among different algorithms and different strength levels was tested by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). The consistency of RFs among different strength levels of the same algorithm and clinically comparable levels across algorithms was evaluated by intraclass correlation coefficient (ICC). Robust features were identified by Kruskal-Wallis and Mann-Whitney test. RESULTS: Among the five reconstruction methods, the mean CV and QCD in the standard-dose group were 0.364 and 0.213, respectively, and the corresponding values were 0.444 and 0.245 in the low-dose group. The mean ICC values between ASIR-V 30% and 70%, DLIR-L and M, DLIR-M and H, DLIR-L and H, ASIR-V30% and DLIR-M, and ASIR-V70% and DLIR-H were 0.672, 0.734, 0.756, 0.629, 0.724, and 0.651, respectively, in the standard-dose group, and the corresponding values were 0.500, 0.567, 0.700, 0.474, 0.499, and 0.650 in the low-dose group. The ICC values between DLIR-M and H under low-dose conditions were even higher than those of ASIR-V30% and -V70% under standard dose conditions. Among the five reconstruction settings, averages of 14.0% (117/837) and 10.3% (86/837) of RFs across 18 ROIs exhibited robustness under standard-dose and low-dose conditions, respectively. Some 23.1% (193/837) of RFs demonstrated robustness between the low-dose DLIR-M and H groups, which was higher than the 21.0% (176/837) observed in the standard-dose ASIR-V30% and -V70% groups. CONCLUSIONS: Most of the RFs lacked reproducibility across algorithms and energy levels. However, DLIR at medium (M) and high (H) levels significantly improved RFs consistency and robustness, even at reduced doses.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/00b0/12397659/cf9f3587353c/qims-15-09-7922-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/00b0/12397659/70f978d3d87d/qims-15-09-7922-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/00b0/12397659/28133bcc2f2f/qims-15-09-7922-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/00b0/12397659/cf9f3587353c/qims-15-09-7922-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/00b0/12397659/70f978d3d87d/qims-15-09-7922-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/00b0/12397659/28133bcc2f2f/qims-15-09-7922-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/00b0/12397659/cf9f3587353c/qims-15-09-7922-f3.jpg

相似文献

[1]
Impact of a deep learning image reconstruction algorithm on the robustness of abdominal computed tomography radiomics features using standard and low radiation doses.

Quant Imaging Med Surg. 2025-9-1

[2]
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[6]
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[9]
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[10]
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本文引用的文献

[1]
Robustness of radiomics within photon-counting detector CT: impact of acquisition and reconstruction factors.

Eur Radiol. 2025-1-31

[2]
Low tube voltage and deep-learning reconstruction for reducing radiation and contrast medium doses in thin-slice abdominal CT: a prospective clinical trial.

Eur Radiol. 2024-11

[3]
Reproducibility in Radiomics: A Comparison of Feature Extraction Methods and Two Independent Datasets.

Appl Sci (Basel). 2024-2-20

[4]
Impacts of Adaptive Statistical Iterative Reconstruction-V and Deep Learning Image Reconstruction Algorithms on Robustness of CT Radiomics Features: Opportunity for Minimizing Radiomics Variability Among Scans of Different Dose Levels.

J Imaging Inform Med. 2024-2

[5]
Low-dose liver CT: image quality and diagnostic accuracy of deep learning image reconstruction algorithm.

Eur Radiol. 2024-4

[6]
A Deep Learning Image Reconstruction Algorithm for Improving Image Quality and Hepatic Lesion Detectability in Abdominal Dual-Energy Computed Tomography: Preliminary Results.

J Digit Imaging. 2023-12

[7]
Deep learning reconstruction CT for liver metastases: low-dose dual-energy vs standard-dose single-energy.

Eur Radiol. 2024-1

[8]
Robustness of radiomics features of virtual unenhanced and virtual monoenergetic images in dual-energy CT among different imaging platforms and potential role of CT number variability.

Insights Imaging. 2023-5-11

[9]
Impact of deep learning image reconstruction algorithms on CT radiomic features in patients with liver tumors.

Front Oncol. 2023-4-5

[10]
CT texture analysis reliability in pulmonary lesions: the influence of 3D vs. 2D lesion segmentation and volume definition by a Hounsfield-unit threshold.

Eur Radiol. 2023-5

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