Impact of deep learning reconstruction algorithms on image quality of chest CT and reproducibility of lung nodule radiomics feature data
10.13929/j.issn.1003-3289.2025.01.017
- VernacularTitle:深度学习重建算法对胸部CT图像质量及肺结节影像组学特征数据可重复性的影响
- Author:
Zhijuan ZHENG
1
;
Shulin LI
;
Kun MA
;
Zhiming XIANG
Author Information
1. 广州医科大学附属番禺中心医院放射科,广东 广州 511400
- Publication Type:Journal Article
- Keywords:
deep learning;
lung diseases;
tomography,X-ray computed;
radiomics;
prospective studies
- From:
Chinese Journal of Medical Imaging Technology
2025;41(1):79-83
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the impact of deep learning image reconstruction(DLIR)algorithms on image quality of chest CT,detection rate of lung nodule and reproducibility of lung nodule radiomics feature data compared with adaptive statistical iterative reconstruction V(ASIR-V)algorithms.Methods Seventy-five patients with 211 lung nodules who underwent both ultra-low-dose CT(ULD-CT)and standard-dose CT(SDCT)were prospectively enrolled.ULD-CT images were reconstructed using different algorithms,namely high-level DLIR(DLIR-H),medium-level DLIR(DLIR-M)and 50％ASIR-V(50％ASIR-V),while SDCT images were reconstructed by 50％ASIR-V.Image noise was represented by the standard deviation(SD)of lung parenchyma CT values within identical ROI in both ULD-CT and SDCT images,and signal-to-noise ratio(SNR)were calculated.The detection rate of lung nodule were obtained and compared among different images.Radiomics features of lung nodules in chest 50％ASIR-V SDCT and each ULD-CT were extracted based on automatic segmentation methods,and intra-class correlation coefficients(ICC)of each ULD-CT and 50％ASIR-V SDCT were calculated respectively,and then compared among different ULD-CT algorithms.Results Compared with SDCT images reconstructed with 50％ASIR-V algorithm,all ULD-CT images reconstructed by different algorithms showed higher SD and lower SNR(all P＜0.05).ULD-CT images reconstructed by DLIR-H,DLIR-M and 50％ASIR-V exhibited progressively increasing SD and decreasing SNR(all adjusted P＜0.05).Taken 50％ASIR-V SDCT images as standards,ULD-CT by DLIR-H,DLIR-M and 50％ASIR-V each detected 207 lung nodules(207/211,98.10％),respectively.In chest ULD-CT images,the reproducibility with 50％ASIR-V SDCT for texture feature data of lung nodules on ULD-CT reconstructed by 50％ASIR-V algorithm was lower than that by DLIR-H and DLIR-M(both adjusted P＜0.05),while no significant difference was found between the latter two with 50％ASIR-V SDCT(adjusted P＞0.05).The first order and shape feature data of lung nodules on ULD-CT reconstructed by all 3 algorithms showed good reproducibility with 50％ASIR-V SDCT(median ICC＞0.75),and no significant difference was detected among them(all P＞0.05).Conclusion Compared with 50％ASIR-V ULD-CT,both DLIR-H and DLIR-M ULD-CT could significantly reduce image noise and improve image quality,as well as maintain reproducibility of radiomics features in lung nodules in a certain degree,especially DLIR-H.
