A comparison between manual and artificial intelligence-based automatic positioning in CT imaging for COVID-19 patients

Yadong Gang; Xiongfeng Chen; Huan Li; Hanlun Wang; Jianying Li; Ying Guo; Junjie Zeng; Qiang Hu; Jinxiang Hu; Haibo Xu

doi:10.1007/s00330-020-07629-4

A comparison between manual and artificial intelligence-based automatic positioning in CT imaging for COVID-19 patients

Eur Radiol. 2021 Aug;31(8):6049-6058. doi: 10.1007/s00330-020-07629-4. Epub 2021 Mar 19.

Authors

Yadong Gang¹, Xiongfeng Chen², Huan Li¹, Hanlun Wang¹, Jianying Li³, Ying Guo³, Junjie Zeng¹, Qiang Hu¹, Jinxiang Hu¹, Haibo Xu⁴

Affiliations

¹ Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, NO.169 Donghu Road, Wuchang District, Wuhan, 430071, Hubei Province, People's Republic of China.
² Department of Radiology, Puren Hospital affiliated to Wuhan University of Science and Technology, NO.1 Benxi street, Jianshe 4th Road, Qingshan District, Wuhan, 430080, Hubei Province, People's Republic of China.
³ GE Healthcare, Computed Tomography Research Center, Beijing, 100176, People's Republic of China.
⁴ Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, NO.169 Donghu Road, Wuchang District, Wuhan, 430071, Hubei Province, People's Republic of China. xuhaibo1120@hotmail.com.

Abstract

Objective: To analyze and compare the imaging workflow, radiation dose, and image quality for COVID-19 patients examined using either the conventional manual positioning (MP) method or an AI-based automatic positioning (AP) method.

Materials and methods: One hundred twenty-seven adult COVID-19 patients underwent chest CT scans on a CT scanner using the same scan protocol except with the manual positioning (MP group) for the initial scan and an AI-based automatic positioning method (AP group) for the follow-up scan. Radiation dose, patient positioning time, and off-center distance of the two groups were recorded and compared. Image noise and signal-to-noise ratio (SNR) were assessed by three experienced radiologists and were compared between the two groups.

Results: The AP operation was successful for all patients in the AP group and reduced the total positioning time by 28% compared with the MP group. Compared with the MP group, the AP group had significantly less patient off-center distance (AP 1.56 cm ± 0.83 vs. MP 4.05 cm ± 2.40, p < 0.001) and higher proportion of positioning accuracy (AP 99% vs. MP 92%), resulting in 16% radiation dose reduction (AP 6.1 mSv ± 1.3 vs. MP 7.3 mSv ± 1.2, p < 0.001) and 9% image noise reduction in erector spinae and lower noise and higher SNR for lesions in the pulmonary peripheral areas.

Conclusion: The AI-based automatic positioning and centering in CT imaging is a promising new technique for reducing radiation dose and optimizing imaging workflow and image quality in imaging the chest.

Key points: • The AI-based automatic positioning (AP) operation was successful for all patients in our study. • AP method reduced the total positioning time by 28% compared with the manual positioning (MP). • AP method had less patient off-center distance and higher proportion of positioning accuracy than MP method, resulting in 16% radiation dose reduction and 9% image noise reduction in erector spinae.

Keywords: Artificial intelligence; Coronavirus; Radiation dosage; Tomography.

MeSH terms

Adult
Artificial Intelligence*
COVID-19*
Humans
Radiation Dosage
SARS-CoV-2
Tomography, X-Ray Computed

Abstract

MeSH terms

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