| Yang Zhe,Zhang Yiting,Bao Yonghua,et al.Application of AI-based automatic collimation in chest digital radiography[J].Chinese Journal of Radiological Medicine and Protection,2026,46(7):707-711 |
| Application of AI-based automatic collimation in chest digital radiography |
| Received:October 27, 2025 |
| DOI：10.3760/cma.j.cn112271-20251027-00378 |
| KeyWords:Digital radiography Artificial intelligence Automatic collimation Intelligent positioning Radiation dose |
| FundProject: |
| Author Name | Affiliation | E-mail | | Yang Zhe | Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China | | | Zhang Yiting | Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China | | | Bao Yonghua | Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China | | | Tong Xueming | Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China | | | Liu Xingyu | Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China | | | Wang Qidong | Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China | wangqidong@zju.edu.cn |
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| Abstract:: |
| Objective To investigate the application value of artificial intelligence (AI)-based automatic collimation in radiation dose control and lung field localization accuracy optimization in chest digital radiography. Methods A total of 3 048 patients who underwent chest digital radiography were retrospectively enrolled via simple random sampling. All patients were examined with the X-ray exposure parameters fixed at 125 kV and automatic exposure control mode. The AI control group (n=1 524) underwent AI-based positioning and automatic collimation, while the control group (n=1 524) was examined with conventional positioning and manual collimation. The dose area product, collimation area, center deviation between collimation field and lung field, and image quality scores were compared between the two groups. Results The dose area product in the AI control group was [0.66 (0.50, 0.78) dGy·cm2], which was lower than that in the control group [0.86 (0.70, 1.05) dGy·cm2 ] (Z=-24.98, P<0.01). The size of the collimation area in the AI control group was [1 578.10 (1 419.85, 1 711.55) cm2], which was significantly lower than that in the control group [1 600.00 (1 496.36, 1 720.00) cm2 ] (Z=-5.89, P<0.01). The center deviation between the collimation field and lung field in the AI control group was [1.48 (0.85, 2.24) cm], which was also lower than that in the control group [1.58 (1.01, 2.43) cm ] (Z= -4.19, P<0.01). The image quality scores of the AI control group were all above 4 points, with no statistically significant difference compared to the control group (P>0.05). Conclusions The application of AI-based automatic collimation in chest digital radiography can significantly reduce the radiation dose to examinees, effectively decrease the collimation area, and improve the localization accuracy of the lung field center. |
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