| Xu Yayun,Hu Zhengyang,Lyu Pin,Yang Wen,Xin Xiaoyan,Yang Shangwen,Chen Xingbiao.Feasibility of artificial intelligence diagnosis of pulmonary nodules on virtual non-contrast images derived from dual-layer spectral detector CT[J].Chinese Journal of Radiological Medicine and Protection,2023,43(10):827-832 |
| Feasibility of artificial intelligence diagnosis of pulmonary nodules on virtual non-contrast images derived from dual-layer spectral detector CT |
| Received:February 24, 2023 |
| DOI：10.3760/cma.j.cn112271-20230224-00052 |
| KeyWords:Computed tomography Artificial intelligence Lung nodule Radiation dosage Virtual non-contrast |
| FundProject: |
| Author Name | Affiliation | E-mail | | Xu Yayun | Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China | | | Hu Zhengyang | Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China | | | Lyu Pin | Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China | | | Yang Wen | Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China | | | Xin Xiaoyan | Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China | | | Yang Shangwen | Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China | 13584038729@126.com | | Chen Xingbiao | Clinical Science, Philips Healthcare, Shanghai 200070, China | |
|
| Hits: 771 |
| Download times: 308 |
| Abstract:: |
| Objective To evaluate the feasibility of artificial intelligence (AI) diagnosis of pulmonary nodules on virtual non-contrast(VNC) images derived from dual-layer detector spectral CT. Methods Totally 52 patients who underwent non-contrast and dual-phase enhanced chest CT scan from May 2022 to November 2022 were enrolled in this study. The VNC images of lung were reconstructed based on venous phase data. CT values and image noise of lung parenchyma, signal-to-noise ratio (SNR) were measured. The dose-length product (DLP) of each scan was recorded and the effective dose (E) was calculated. All of the objective indicators of image quality and radiation dose were compared by Paired t test. Image quality was evaluated subjectively by two radiologists and compared with Wilcoxon non-parametric test. Wilcoxon symbolic rank test was used to compare the sensitivity and false positive detection rate (FPDR) of AI diagnosis between two groups. Results Compared with TNC, the noise of venous VNC image was decreased by 13.8%, SNR increased by 14.9%, and both of DLP and E decreased by 33.3% (t=5.82, -5.35, 22.93, 22.92,P <0.05). There were no significant differences in CT values and subjective scores between 2 groups (P >0.05). For different types of pulmonary nodules, there was no statistical difference in the sensitivity of AI diagnosis between two groups (P >0.05). For solid nodules with diameter ≤4 mm and all pulmonary nodules in general, FPDR in VNC group was slightly increased with statistical significance (Z=-2.03, -3.09,P<0.05), while for other types of pulmonary nodules, there was no statistical difference (P >0.05). Conclusions The VNC images of thoracic venous phase based on spectral CT can significantly reduce the radiation dose of patients while the image quality and the AI diagnostic sensitivity of pulmonary nodules remain unchanged, and the FPDR without significantly increase. And it could replace TNC for daily routine. |
| HTML View Full Text View/Add Comment Download reader |
| Close |
|
|
|
