Reduced-dose deep learning iterative reconstruction for abdominal computed tomography with low tube voltage and tube current
Abstract Background The low tube-voltage technique (e.g., 80 kV) can efficiently reduce the radiation dose and increase the contrast enhancement of vascular and parenchymal structures in abdominal CT. However, a high tube current is always required in this setting and limits the dose reduction poten...
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2024-12-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-024-02811-w |
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| author | Shumeng Zhu Baoping Zhang Qian Tian Ao Li Zhe Liu Wei Hou Wenzhe Zhao Xin Huang Yao Xiao Yiming Wang Rui Wang Yuhang Li Jian Yang Chao Jin |
| author_facet | Shumeng Zhu Baoping Zhang Qian Tian Ao Li Zhe Liu Wei Hou Wenzhe Zhao Xin Huang Yao Xiao Yiming Wang Rui Wang Yuhang Li Jian Yang Chao Jin |
| author_sort | Shumeng Zhu |
| collection | DOAJ |
| description | Abstract Background The low tube-voltage technique (e.g., 80 kV) can efficiently reduce the radiation dose and increase the contrast enhancement of vascular and parenchymal structures in abdominal CT. However, a high tube current is always required in this setting and limits the dose reduction potential. This study investigated the feasibility of a deep learning iterative reconstruction algorithm (Deep IR) in reducing the radiation dose while improving the image quality for abdominal computed tomography (CT) with low tube voltage and current. Methods Sixty patients (male/female, 36/24; Age, 57.72 ± 10.19 years) undergoing the abdominal portal venous phase CT were randomly divided into groups A (100 kV, automatic exposure control [AEC] with reference tube-current of 213 mAs) and B (80 kV, AEC with reference of 130 mAs). Images were reconstructed via hybrid iterative reconstruction (HIR) and Deep IR (levels 1–5). The mean CT and standard deviation (SD) values of four regions of interest (ROI), i.e. liver, spleen, main portal vein and erector spinae at the porta hepatis level in each image serial were measured, and the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. The image quality was subjectively scored by two radiologists using a 5-point criterion. Results A significant reduction in the radiation dose of 69.94% (5.09 ± 0.91 mSv vs. 1.53 ± 0.37 mSv) was detected in Group B compared with Group A. After application of the Deep IR, there was no significant change in the CT value, but the SD gradually increased. Group B had higher CT values than group A, and the portal vein CT values significantly differed between the groups (P < 0.003). The SNR and CNR in Group B with Deep IR at levels 1–5 were greater than those in Group A and significantly differed when HIR and Deep IR were applied at levels 1–3 of HIR and Deep IR (P < 0.003). The subjective scores (distortion, clarity of the portal vein, visibility of small structures and overall image quality) with Deep IR at levels 4–5 in Group B were significantly higher than those in group A with HIR (P < 0.003). Conclusion Deep IR algorithm can meet the clinical requirements and reduce the radiation dose by 69.94% in portal venous phase abdominal CT with a low tube voltage of 80 kV and a low tube current. Deep IR at levels 4–5 can significantly improve the image quality of the abdominal parenchymal organs and the clarity of the portal vein. |
| format | Article |
| id | doaj-art-d3fe197ff3a34ed6ac4f6df37cc35f2b |
| institution | OA Journals |
| issn | 1472-6947 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
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| series | BMC Medical Informatics and Decision Making |
| spelling | doaj-art-d3fe197ff3a34ed6ac4f6df37cc35f2b2025-08-20T01:59:43ZengBMCBMC Medical Informatics and Decision Making1472-69472024-12-012411910.1186/s12911-024-02811-wReduced-dose deep learning iterative reconstruction for abdominal computed tomography with low tube voltage and tube currentShumeng Zhu0Baoping Zhang1Qian Tian2Ao Li3Zhe Liu4Wei Hou5Wenzhe Zhao6Xin Huang7Yao Xiao8Yiming Wang9Rui Wang10Yuhang Li11Jian Yang12Chao Jin13Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong UniversityDepartment of Radiology, The First Affiliated Hospital of Xi’an Jiaotong UniversityDepartment of Radiology, The First Affiliated Hospital of Xi’an Jiaotong UniversityDepartment of Radiology, The First Affiliated Hospital of Xi’an Jiaotong UniversityDepartment of Radiology, The First Affiliated Hospital of Xi’an Jiaotong UniversityDepartment of Radiology, The First Affiliated Hospital of Xi’an Jiaotong UniversityDepartment of Radiology, The First Affiliated Hospital of Xi’an Jiaotong UniversityDepartment of Radiology, The First Affiliated Hospital of Xi’an Jiaotong UniversityDepartment of Radiology, The First Affiliated Hospital of Xi’an Jiaotong UniversityDepartment of Radiology, The First Affiliated Hospital of Xi’an Jiaotong UniversityDepartment of Radiology, The First Affiliated Hospital of Xi’an Jiaotong UniversityDepartment of Radiology, The First Affiliated Hospital of Xi’an Jiaotong UniversityDepartment of Radiology, The First Affiliated Hospital of Xi’an Jiaotong UniversityDepartment of Radiology, The First Affiliated Hospital of Xi’an Jiaotong UniversityAbstract Background The low tube-voltage technique (e.g., 80 kV) can efficiently reduce the radiation dose and increase the contrast enhancement of vascular and parenchymal structures in abdominal CT. However, a high tube current is always required in this setting and limits the dose reduction potential. This study investigated the feasibility of a deep learning iterative reconstruction algorithm (Deep IR) in reducing the radiation dose while improving the image quality for abdominal computed tomography (CT) with low tube voltage and current. Methods Sixty patients (male/female, 36/24; Age, 57.72 ± 10.19 years) undergoing the abdominal portal venous phase CT were randomly divided into groups A (100 kV, automatic exposure control [AEC] with reference tube-current of 213 mAs) and B (80 kV, AEC with reference of 130 mAs). Images were reconstructed via hybrid iterative reconstruction (HIR) and Deep IR (levels 1–5). The mean CT and standard deviation (SD) values of four regions of interest (ROI), i.e. liver, spleen, main portal vein and erector spinae at the porta hepatis level in each image serial were measured, and the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. The image quality was subjectively scored by two radiologists using a 5-point criterion. Results A significant reduction in the radiation dose of 69.94% (5.09 ± 0.91 mSv vs. 1.53 ± 0.37 mSv) was detected in Group B compared with Group A. After application of the Deep IR, there was no significant change in the CT value, but the SD gradually increased. Group B had higher CT values than group A, and the portal vein CT values significantly differed between the groups (P < 0.003). The SNR and CNR in Group B with Deep IR at levels 1–5 were greater than those in Group A and significantly differed when HIR and Deep IR were applied at levels 1–3 of HIR and Deep IR (P < 0.003). The subjective scores (distortion, clarity of the portal vein, visibility of small structures and overall image quality) with Deep IR at levels 4–5 in Group B were significantly higher than those in group A with HIR (P < 0.003). Conclusion Deep IR algorithm can meet the clinical requirements and reduce the radiation dose by 69.94% in portal venous phase abdominal CT with a low tube voltage of 80 kV and a low tube current. Deep IR at levels 4–5 can significantly improve the image quality of the abdominal parenchymal organs and the clarity of the portal vein.https://doi.org/10.1186/s12911-024-02811-wLow tube voltageDeep learning iterative reconstruction algorithmRadiation doseImage qualityAbdomenPortal vein |
| spellingShingle | Shumeng Zhu Baoping Zhang Qian Tian Ao Li Zhe Liu Wei Hou Wenzhe Zhao Xin Huang Yao Xiao Yiming Wang Rui Wang Yuhang Li Jian Yang Chao Jin Reduced-dose deep learning iterative reconstruction for abdominal computed tomography with low tube voltage and tube current BMC Medical Informatics and Decision Making Low tube voltage Deep learning iterative reconstruction algorithm Radiation dose Image quality Abdomen Portal vein |
| title | Reduced-dose deep learning iterative reconstruction for abdominal computed tomography with low tube voltage and tube current |
| title_full | Reduced-dose deep learning iterative reconstruction for abdominal computed tomography with low tube voltage and tube current |
| title_fullStr | Reduced-dose deep learning iterative reconstruction for abdominal computed tomography with low tube voltage and tube current |
| title_full_unstemmed | Reduced-dose deep learning iterative reconstruction for abdominal computed tomography with low tube voltage and tube current |
| title_short | Reduced-dose deep learning iterative reconstruction for abdominal computed tomography with low tube voltage and tube current |
| title_sort | reduced dose deep learning iterative reconstruction for abdominal computed tomography with low tube voltage and tube current |
| topic | Low tube voltage Deep learning iterative reconstruction algorithm Radiation dose Image quality Abdomen Portal vein |
| url | https://doi.org/10.1186/s12911-024-02811-w |
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