Value of Low Tube Voltage Combined with Deep Learning Image Reconstruction Algorithm to Reduce Radiation Dose in Combined Thoracoabdominal Enhanced CT
Objective: To investigate the effect of low tube voltage combined with deep learning image reconstruction (DLIR) on radiation dose reduction and maintaining image quality in combined chest and abdominal enhanced CT scans. Methods: (1) Phantom study. To determine the feasibility of combining low tube...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Editorial Office of Computerized Tomography Theory and Application
2025-05-01
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| Series: | CT Lilun yu yingyong yanjiu |
| Subjects: | |
| Online Access: | https://www.cttacn.org.cn/cn/article/doi/10.15953/j.ctta.2025.001 |
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| Summary: | Objective: To investigate the effect of low tube voltage combined with deep learning image reconstruction (DLIR) on radiation dose reduction and maintaining image quality in combined chest and abdominal enhanced CT scans. Methods: (1) Phantom study. To determine the feasibility of combining low tube voltage with deep learning algorithms for low-contrast resolution, Catphan 500 phantoms were scanned under two different conditions. The optimization group used a low tube voltage (80 kV) combined with DLIR for scanning and image reconstruction, while the routine group used a 120 kV tube voltage combined with adaptive statistical iterative reconstruction V (ASiR-V). This study aimed to determine the effectiveness of the optimization group using a low dose (noise index, NI > 9) compared with the routine group using a routine dose (NI=9). (2) Prospective study. A total of 160 patients who underwent routine chest and abdominal enhanced CT scans were prospectively collected and randomly divided into a low-dose optimization group and routine-dose group, with 149 patients ultimately enrolled (61 in the low-dose optimization group and 88 in the routine-dose group). Based on the results of the phantom study, the low-dose optimization group used the optimized condition with NI set to the optimal value, whereas the routine-dose group used the routine condition with NI=9. Radiation doses were recorded and calculated for both groups, and image quality was subjectively and objectively evaluated. Results: The low-dose optimization group using NI=12 achieved an equivalent low-contrast resolution capability to the routine-dose group with NI=9. The effective dose in the low-dose optimization group (9.56±2.34) mSv was significantly lower than that in the routine-dose group (17.82±5.22) mSv. The liver and aorta attenuation values in the low-dose optimization group were significantly higher than those in the routine-dose group, and the CNR and SNR values in the liver and aorta were also significantly higher. The spatial resolution of the aorta, common hepatic artery, and portal vein and the display of small vessels/bronchi were all superior in the low-dose optimization group compared with the routine-dose group. Conclusion: The combination of a low tube voltage and deep learning image reconstruction algorithm can ensure equivalent or even higher image quality while reducing radiation dose, providing a feasible solution for optimizing radiation dose in large-scale CT scans such as the combined thoracoabdominal enhanced CT. |
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| ISSN: | 1004-4140 |