Feasibility Study of Triple-low CCTA for Coronary Artery Disease Screening Combining Contrast Enhancement Boost and Deep Learning Reconstruction
Background: The aim of this study was to compare the image quality of coronary computed tomography angiography (CCTA) images obtained using contrast enhancement boost (CE-boost) technology combined with deep learning reconstruction technology at a low dose and low contrast agent f...
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IMR Press
2025-06-01
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| Series: | Reviews in Cardiovascular Medicine |
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| Online Access: | https://www.imrpress.com/journal/RCM/26/6/10.31083/RCM31334 |
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| author | Zhihua Wu Min Chen Yingwen Wei Chen Shen Wen Han Rulin Xu Zhenyuan Zhou Jiexiong Xu |
| author_facet | Zhihua Wu Min Chen Yingwen Wei Chen Shen Wen Han Rulin Xu Zhenyuan Zhou Jiexiong Xu |
| author_sort | Zhihua Wu |
| collection | DOAJ |
| description | Background: The aim of this study was to compare the image quality of coronary computed tomography angiography (CCTA) images obtained using contrast enhancement boost (CE-boost) technology combined with deep learning reconstruction technology at a low dose and low contrast agent flow rate/dosage with traditional CCTA images, while exploring the potential application of this technology in early screening of coronary artery disease. Methods: From March 2024 to September 2024, 60 patients suspected of having coronary artery stenosis were enrolled in this study. Ultimately, 46 patients were included for analysis. Based on different acquisition protocols, divided into Group A and Group B. Group A underwent conventional computed tomography (CT) angiography with a tube voltage of 120 kV, a contrast agent injection rate of 6 mL/s, and a dosage of 0.9 mL/kg. Group B received a triple-low CCTA protocol with a tube voltage of 100 kV, a contrast agent injection rate of 2 mL/s, and a dosage of 0.3 mL/kg. Additionally, Group C was created by applying CE-Boost combined with a deep learning reconstruction technique to Group B images. The radiation dose and contrast agent dosage were compared between Group A and Group B. The image quality of the three groups, including CT values, background noise, signal-to-noise ratio (SNR), and contrast signal-to-noise ratio (CNR), was also compared, with p < 0.05 indicating significant statistical differences. Results: Our results indicate that Group A required 67.8% more contrast agent and a 52.0% higher radiation dose than Group B (64.68 ± 3.30 mL vs. 20.19 ± 2.22 mL, 6.21 (4.60, 7.78) mSv vs. 2.05 (1.42, 4.33) mSv, all p < 0.05). Image analysis revealed superior subjective scores in Groups A (4.68 ± 0.72) and C (4.38 ± 0.95) versus Group B (4.25 ± 0.10) (both p < 0.05), with no statistical difference between Groups A and C. CT values were significantly elevated in Group A across all vessels compared to both Groups B and C (p < 0.05), while Group C exceeded Group B post CE-Boost. SNR comparisons showed Group A dominance over B in the proximal right coronary artery (RCA-1)/left main coronary artery (LM)/left anterior descending coronary artery (LAD)/left circumflex coronary artery (LCX) and over C in the RCA-1/LM (p < 0.05), contrasting with the superiority of SNR in Group C versus B in the middle right coronary artery/distal right coronary artery (RCA-2/3)/LM/LAD/LCX. CNR analysis demonstrated an equivalent performance between A and C, though both groups significantly surpassed Group B (A vs. B: p < 0.05; C vs. B: p < 0.05). Conclusion: The triple-low CCTA protocol using CE-Boost technology combined with deep learning reconstruction, achieved a 52% reduction in radiation exposure and a 67.8% reduction in contrast agent usage, while preserving diagnostic image quality (with CNR and noise levels comparable to standard protocols). This demonstrates its clinical feasibility for repeated coronary evaluations without compromising diagnostic accuracy. |
| format | Article |
| id | doaj-art-bc0d08cd5d794182b3304410bab89f2c |
| institution | DOAJ |
| issn | 1530-6550 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | IMR Press |
| record_format | Article |
| series | Reviews in Cardiovascular Medicine |
| spelling | doaj-art-bc0d08cd5d794182b3304410bab89f2c2025-08-20T02:42:53ZengIMR PressReviews in Cardiovascular Medicine1530-65502025-06-012663133410.31083/RCM31334S1530-6550(25)01817-4Feasibility Study of Triple-low CCTA for Coronary Artery Disease Screening Combining Contrast Enhancement Boost and Deep Learning ReconstructionZhihua Wu0Min Chen1Yingwen Wei2Chen Shen3Wen Han4Rulin Xu5Zhenyuan Zhou6Jiexiong Xu7Department of Radiology, The Fourth Affiliated Hospital, Guangzhou Medical University, 511300 Guangzhou, Guangdong, ChinaDepartment of Radiology, The Fourth Affiliated Hospital, Guangzhou Medical University, 511300 Guangzhou, Guangdong, ChinaDepartment of Radiology, The Fourth Affiliated Hospital, Guangzhou Medical University, 511300 Guangzhou, Guangdong, ChinaDepartment of Radiology, The Fourth Affiliated Hospital, Guangzhou Medical University, 511300 Guangzhou, Guangdong, ChinaDepartment of Radiology, The Fourth Affiliated Hospital, Guangzhou Medical University, 511300 Guangzhou, Guangdong, ChinaResearch Collaboration, Canon Medical Systems, 510623 Guangzhou, Guangdong, ChinaDepartment of Radiology, The Fourth Affiliated Hospital, Guangzhou Medical University, 511300 Guangzhou, Guangdong, ChinaDepartment of Radiology, The Fourth Affiliated Hospital, Guangzhou Medical University, 511300 Guangzhou, Guangdong, ChinaBackground: The aim of this study was to compare the image quality of coronary computed tomography angiography (CCTA) images obtained using contrast enhancement boost (CE-boost) technology combined with deep learning reconstruction technology at a low dose and low contrast agent flow rate/dosage with traditional CCTA images, while exploring the potential application of this technology in early screening of coronary artery disease. Methods: From March 2024 to September 2024, 60 patients suspected of having coronary artery stenosis were enrolled in this study. Ultimately, 46 patients were included for analysis. Based on different acquisition protocols, divided into Group A and Group B. Group A underwent conventional computed tomography (CT) angiography with a tube voltage of 120 kV, a contrast agent injection rate of 6 mL/s, and a dosage of 0.9 mL/kg. Group B received a triple-low CCTA protocol with a tube voltage of 100 kV, a contrast agent injection rate of 2 mL/s, and a dosage of 0.3 mL/kg. Additionally, Group C was created by applying CE-Boost combined with a deep learning reconstruction technique to Group B images. The radiation dose and contrast agent dosage were compared between Group A and Group B. The image quality of the three groups, including CT values, background noise, signal-to-noise ratio (SNR), and contrast signal-to-noise ratio (CNR), was also compared, with p < 0.05 indicating significant statistical differences. Results: Our results indicate that Group A required 67.8% more contrast agent and a 52.0% higher radiation dose than Group B (64.68 ± 3.30 mL vs. 20.19 ± 2.22 mL, 6.21 (4.60, 7.78) mSv vs. 2.05 (1.42, 4.33) mSv, all p < 0.05). Image analysis revealed superior subjective scores in Groups A (4.68 ± 0.72) and C (4.38 ± 0.95) versus Group B (4.25 ± 0.10) (both p < 0.05), with no statistical difference between Groups A and C. CT values were significantly elevated in Group A across all vessels compared to both Groups B and C (p < 0.05), while Group C exceeded Group B post CE-Boost. SNR comparisons showed Group A dominance over B in the proximal right coronary artery (RCA-1)/left main coronary artery (LM)/left anterior descending coronary artery (LAD)/left circumflex coronary artery (LCX) and over C in the RCA-1/LM (p < 0.05), contrasting with the superiority of SNR in Group C versus B in the middle right coronary artery/distal right coronary artery (RCA-2/3)/LM/LAD/LCX. CNR analysis demonstrated an equivalent performance between A and C, though both groups significantly surpassed Group B (A vs. B: p < 0.05; C vs. B: p < 0.05). Conclusion: The triple-low CCTA protocol using CE-Boost technology combined with deep learning reconstruction, achieved a 52% reduction in radiation exposure and a 67.8% reduction in contrast agent usage, while preserving diagnostic image quality (with CNR and noise levels comparable to standard protocols). This demonstrates its clinical feasibility for repeated coronary evaluations without compromising diagnostic accuracy.https://www.imrpress.com/journal/RCM/26/6/10.31083/RCM31334ce-boostdeep learning reconstructioncoronary artery diseasecoronary ctalow radiation dose |
| spellingShingle | Zhihua Wu Min Chen Yingwen Wei Chen Shen Wen Han Rulin Xu Zhenyuan Zhou Jiexiong Xu Feasibility Study of Triple-low CCTA for Coronary Artery Disease Screening Combining Contrast Enhancement Boost and Deep Learning Reconstruction Reviews in Cardiovascular Medicine ce-boost deep learning reconstruction coronary artery disease coronary cta low radiation dose |
| title | Feasibility Study of Triple-low CCTA for Coronary Artery Disease Screening Combining Contrast Enhancement Boost and Deep Learning Reconstruction |
| title_full | Feasibility Study of Triple-low CCTA for Coronary Artery Disease Screening Combining Contrast Enhancement Boost and Deep Learning Reconstruction |
| title_fullStr | Feasibility Study of Triple-low CCTA for Coronary Artery Disease Screening Combining Contrast Enhancement Boost and Deep Learning Reconstruction |
| title_full_unstemmed | Feasibility Study of Triple-low CCTA for Coronary Artery Disease Screening Combining Contrast Enhancement Boost and Deep Learning Reconstruction |
| title_short | Feasibility Study of Triple-low CCTA for Coronary Artery Disease Screening Combining Contrast Enhancement Boost and Deep Learning Reconstruction |
| title_sort | feasibility study of triple low ccta for coronary artery disease screening combining contrast enhancement boost and deep learning reconstruction |
| topic | ce-boost deep learning reconstruction coronary artery disease coronary cta low radiation dose |
| url | https://www.imrpress.com/journal/RCM/26/6/10.31083/RCM31334 |
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