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Comparative study of image quality and radiation dose in thoracic-abdominal-pelvic CT Enhancement with different tube voltages and reconstruction algorithms

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机构: [1]Hebei Univ, Dept Radiol, Affiliated Hosp, Baoding 071000, Hebei, Peoples R China
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关键词: Artificial intelligence iterative reconstruction Thoracic-abdominal-pelvic CT enhancement Optimal noise level Image quality Radiation dose

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Objectives: To evaluate the image quality and radiation dose in thoracic-abdominal-pelvic computed tomography enhancement using ATVM (Automatic Tube Voltage Modulation)coupled with AIIR versus routine tube voltage combined with Karl-3D IR. The optimal noise level for the AIIR in thoracic-abdominal-pelvic CT enhancement was also determined. Methods: Group A was scanned using ATVM, and images were reconstructed using AIIR with 1-5 noise levels. Group B was scanned and reconstructed using the fixed tube voltage (120 kVp) combined with Karl 3D level 5 IR. The image quality of the reconstructed images of AIIR with 1-5 noise levels were compared and the best image reconstruction noise level for AIIR was preferred. Image quality and radiation dose were statistically analyzed for group A (best image reconstruction noise level for AIIR) and group B. Results: AIIR level 3 is the optimal noise level for CT-enhanced image reconstruction of the thorax, abdomen, and pelvis. The mean SNR, CNR, and subjective evaluation of AIIR level 3 Group A images were better than those of Karl 3D level 5 Group B images (p < 0.05). The mean SSDE and the mean ED of the AIIR Group A patients were reduced by 46% and 41%, respectively, compared with those of Group B. Conclusions: ATVM technology combined with the AIIR algorithm improved image quality and reduced patient radiation dose in thoracic-abdominal-pelvic CT scans. The optimal noise level for the reconstruction of high- quality arterial and venous-phase images was AIIR level 3.

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大类 | 4 区 综合性期刊
小类 | 4 区 综合性期刊
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Q2 MULTIDISCIPLINARY SCIENCES

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第一作者机构: [1]Hebei Univ, Dept Radiol, Affiliated Hosp, Baoding 071000, Hebei, Peoples R China
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