Impact of ultra-low dose CT scanning combined with deep learning image reconstruction on quantitative analysis of pulmonary nodules using computer aided diagnostic system
10.13929/j.issn.1672-8475.2024.07.008
- VernacularTitle:超低剂量CT扫描结合深度学习图像重建对计算机辅助诊断系统定量分析肺结节的影响
- Author:
Yuequn DOU
1
;
Haibo WU
;
Yong YU
;
Nan YU
;
Haifeng DUAN
;
Guangming MA
Author Information
1. 陕西中医药大学附属医院呼吸科,陕西 咸阳 712000
- Keywords:
lung neoplasms;
tomography,X-ray computed;
diagnosis,computer-assisted;
deep learning image reconstruction
- From:
Chinese Journal of Interventional Imaging and Therapy
2024;21(7):418-422
- CountryChina
- Language:Chinese
-
Abstract:
Objective To investigate the impact of ultra-low dose CT(ULDCT)scanning combined with deep learning image reconstruction(DLIR)on quantitative analysis of pulmonary nodules using computer aided diagnostic system(CAD).Methods Fifty-six further consultation patients with pulmonary nodules were prospectively enrolled.ULDCT and standard-dose CT(SDCT)were performed.The raw ULDCT images were reconstructed using adaptive statistical iterative reconstruction-V40%(ASIR-V40%)and high-strength DLIR(DLIR-H)to obtain ULDCT-ASIR-V40%(group A)and ULDCT-DLIR-H(group B)images,while SDCT images were reconstructed with ASIR-V40%to obtain SDCT-ASIR-V40%(group C)images.Pulmonary nodules with long diameter of 4-30 mm were selected as the target nodules based on reconstructed images.The nodules were divided into solid nodules,calcified nodules and non-solid nodules by 2 physicians.CAD software was used to evaluate the classification of nodules based on 3 groups of images,and the long diameter,transverse diameter,density,volume and malignant risk were quantitatively analyzed.Results Totally 104 target nodules were selected,including 51 solid nodules,26 calcified nodules and 27 non-solid nodules according to physicians.CAD classified 53 solid,24 calcified and 27 non-solid nodules based on group A and B,while based on group C,CAD classification was consistent with that of physicians'.Compared with group C,the density of solid and calcified nodules,the volume and malignant risk of non-solid nodules judged by CAD in group A decreased,so did the density of calcified nodules in group B(all P＜0.05).No significant difference of the other CAD quantitative parameters of nodules was found among 3 groups(all P＞0.05).Conclusion ULDCT scanning combined with DLIR might underestimate the density of calcified pulmonary nodules judged by CAD,but had no significant impact on the other CAD quantitative parameters.
