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A comparison between manual and artificial intelligence-based automatic positioning in CT imaging for COVID-19 patients.
Gang, Yadong; Chen, Xiongfeng; Li, Huan; Wang, Hanlun; Li, Jianying; Guo, Ying; Zeng, Junjie; Hu, Qiang; Hu, Jinxiang; Xu, Haibo.
  • Gang Y; 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.
  • Chen X; 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.
  • Li H; 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.
  • Wang H; 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.
  • Li J; GE Healthcare, Computed Tomography Research Center, Beijing, 100176, People's Republic of China.
  • Guo Y; GE Healthcare, Computed Tomography Research Center, Beijing, 100176, People's Republic of China.
  • Zeng J; 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.
  • Hu Q; 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.
  • Hu J; 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.
  • Xu H; 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.
Eur Radiol ; 31(8): 6049-6058, 2021 Aug.
Article in English | MEDLINE | ID: covidwho-1141412
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Artificial Intelligence / COVID-19 Type of study: Cohort study / Experimental Studies / Prognostic study / Randomized controlled trials Limits: Adult / Humans Language: English Journal: Eur Radiol Journal subject: Radiology Year: 2021 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Artificial Intelligence / COVID-19 Type of study: Cohort study / Experimental Studies / Prognostic study / Randomized controlled trials Limits: Adult / Humans Language: English Journal: Eur Radiol Journal subject: Radiology Year: 2021 Document Type: Article