Contrast Study of Low Tube Current Combined with Deep Learning Algorithms in Paranasal Sinus CT Imaging
Objective: To explore the application effect of a low tube current combined with a deep learning algorithm in paranasal sinus computed tomography (CT) imaging and to evaluate its advantages in terms of image quality and radiation dose. Methods: Patients who underwent paranasal sinus CT examinations...
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Editorial Office of Computerized Tomography Theory and Application
2025-05-01
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| Series: | CT Lilun yu yingyong yanjiu |
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| Online Access: | https://www.cttacn.org.cn/cn/article/doi/10.15953/j.ctta.2024.288 |
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| author | Yufei SUN Zhaohui ZHONG Xiangming LI Wanbo ZHOU Chensi XU Miao DENG Lixin ZHANG |
| author_facet | Yufei SUN Zhaohui ZHONG Xiangming LI Wanbo ZHOU Chensi XU Miao DENG Lixin ZHANG |
| author_sort | Yufei SUN |
| collection | DOAJ |
| description | Objective: To explore the application effect of a low tube current combined with a deep learning algorithm in paranasal sinus computed tomography (CT) imaging and to evaluate its advantages in terms of image quality and radiation dose. Methods: Patients who underwent paranasal sinus CT examinations at Beijing Friendship Hospital, Capital Medical University, between March and November 2024 were retrospectively collected and divided into three groups: conventional dose group, low tube current with deep clearInfinity (CI) group, and clearView (CV) group. The CT values, SD values, signal-to-noise ratios (SNR), and contrast-to-noise ratios (CNR) of the inferior turbinate mucosa, medial pterygoid muscle, and temporal fossa fat were measured and calculated for each group to objectively assess image quality. In addition, two head and neck radiologists subjectively scored the image quality of the thinnest slice on a 4-point scale. The radiation doses in the conventional and low tube current groups were also compared. Results: A total of 80 patients were included in this study, with 40 in each group. There were no statistically significant differences in the CT values among the three groups for the inferior turbinate mucosa, medial pterygoid muscle, and temporal fossa fat. There were no statistically significant differences in SD values, SNR, and CNR between the conventional-dose and CI groups. However, statistically significant differences were observed in SD values and SNR between the conventional and CV groups, as well as between the CI and CV groups for the inferior turbinate mucosa, medial pterygoid muscle, and temporal fossa fat. For CNR, statistically significant differences were also found between the conventional and CV groups and between the CI and CV groups in the inferior turbinate mucosa and medial pterygoid muscle regions. In terms of subjective image quality scores, the conventional and CI groups scored 3.93±0.26 and 3.88±0.33, respectively, which were significantly higher than the CV group’s score of 2.70±0.46. Additionally, the radiation dose in the low tube current group was reduced by approximately 74.8% compared to that in the conventional group, with a statistically significant difference. Conclusion: Low tube current combined with a deep learning algorithmin paranasal sinus CT imaging can significantly reduce the radiation dose while maintaining image quality. |
| format | Article |
| id | doaj-art-43cd2bfc07fa418c82206ab95b5b375e |
| institution | OA Journals |
| issn | 1004-4140 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Editorial Office of Computerized Tomography Theory and Application |
| record_format | Article |
| series | CT Lilun yu yingyong yanjiu |
| spelling | doaj-art-43cd2bfc07fa418c82206ab95b5b375e2025-08-20T02:26:08ZengEditorial Office of Computerized Tomography Theory and ApplicationCT Lilun yu yingyong yanjiu1004-41402025-05-0134335135810.15953/j.ctta.2024.2882024-288Contrast Study of Low Tube Current Combined with Deep Learning Algorithms in Paranasal Sinus CT ImagingYufei SUN0Zhaohui ZHONG1Xiangming LI2Wanbo ZHOU3Chensi XU4Miao DENG5Lixin ZHANG6Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, ChinaDepartment of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, ChinaDepartment of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, ChinaNeusoft Medical Systems Co., Ltd., Shenyang 110167, ChinaNeusoft Medical Systems Co., Ltd., Shenyang 110167, ChinaDepartment of Radiology, Beijing Bo'ai Hospital, China Rehabilitation Research Center, Beijing 100068, ChinaDepartment of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, ChinaObjective: To explore the application effect of a low tube current combined with a deep learning algorithm in paranasal sinus computed tomography (CT) imaging and to evaluate its advantages in terms of image quality and radiation dose. Methods: Patients who underwent paranasal sinus CT examinations at Beijing Friendship Hospital, Capital Medical University, between March and November 2024 were retrospectively collected and divided into three groups: conventional dose group, low tube current with deep clearInfinity (CI) group, and clearView (CV) group. The CT values, SD values, signal-to-noise ratios (SNR), and contrast-to-noise ratios (CNR) of the inferior turbinate mucosa, medial pterygoid muscle, and temporal fossa fat were measured and calculated for each group to objectively assess image quality. In addition, two head and neck radiologists subjectively scored the image quality of the thinnest slice on a 4-point scale. The radiation doses in the conventional and low tube current groups were also compared. Results: A total of 80 patients were included in this study, with 40 in each group. There were no statistically significant differences in the CT values among the three groups for the inferior turbinate mucosa, medial pterygoid muscle, and temporal fossa fat. There were no statistically significant differences in SD values, SNR, and CNR between the conventional-dose and CI groups. However, statistically significant differences were observed in SD values and SNR between the conventional and CV groups, as well as between the CI and CV groups for the inferior turbinate mucosa, medial pterygoid muscle, and temporal fossa fat. For CNR, statistically significant differences were also found between the conventional and CV groups and between the CI and CV groups in the inferior turbinate mucosa and medial pterygoid muscle regions. In terms of subjective image quality scores, the conventional and CI groups scored 3.93±0.26 and 3.88±0.33, respectively, which were significantly higher than the CV group’s score of 2.70±0.46. Additionally, the radiation dose in the low tube current group was reduced by approximately 74.8% compared to that in the conventional group, with a statistically significant difference. Conclusion: Low tube current combined with a deep learning algorithmin paranasal sinus CT imaging can significantly reduce the radiation dose while maintaining image quality.https://www.cttacn.org.cn/cn/article/doi/10.15953/j.ctta.2024.288deep learning algorithmiterative algorithmparanasal sinus ctlow-dose ct |
| spellingShingle | Yufei SUN Zhaohui ZHONG Xiangming LI Wanbo ZHOU Chensi XU Miao DENG Lixin ZHANG Contrast Study of Low Tube Current Combined with Deep Learning Algorithms in Paranasal Sinus CT Imaging CT Lilun yu yingyong yanjiu deep learning algorithm iterative algorithm paranasal sinus ct low-dose ct |
| title | Contrast Study of Low Tube Current Combined with Deep Learning Algorithms in Paranasal Sinus CT Imaging |
| title_full | Contrast Study of Low Tube Current Combined with Deep Learning Algorithms in Paranasal Sinus CT Imaging |
| title_fullStr | Contrast Study of Low Tube Current Combined with Deep Learning Algorithms in Paranasal Sinus CT Imaging |
| title_full_unstemmed | Contrast Study of Low Tube Current Combined with Deep Learning Algorithms in Paranasal Sinus CT Imaging |
| title_short | Contrast Study of Low Tube Current Combined with Deep Learning Algorithms in Paranasal Sinus CT Imaging |
| title_sort | contrast study of low tube current combined with deep learning algorithms in paranasal sinus ct imaging |
| topic | deep learning algorithm iterative algorithm paranasal sinus ct low-dose ct |
| url | https://www.cttacn.org.cn/cn/article/doi/10.15953/j.ctta.2024.288 |
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