Evaluation of upper abdominal CT image quality at various low radiation doses using dual-neural-network deep learning algorithm(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2026年第7期
- Page:
- 905-909
- Research Field:
- 医学影像物理
- Publishing date:
Info
- Title:
- Evaluation of upper abdominal CT image quality at various low radiation doses using dual-neural-network deep learning algorithm
- Author(s):
- LI Caixia? WANG Jianping1; QI Hongliang2; HUANG Meiyan3; LI Dianyu1; ZHOU Jianwei1
- 1. Department of Imaging Diagnosis, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China 2. Department of Medical Engineering, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China 3. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
- Keywords:
- Keywords: computed tomography wide-detector deep learning reconstruction algorithm low-dose image quality
- PACS:
- R318；R811.1
- DOI:
- DOI:10.3969/j.issn.1005-202X.2026.07.010
- Abstract:
- Abstract: Objective To investigate the image quality optimization performance of a domestic wide-detector ultra-high-resolution CT combined with a dual-neural-network deep learning reconstruction technique (DLIR-CI) on upper abdominal CT scans acquired with different radiation dose protocols. Methods?hirty-three patients undergoing non-contrast upper abdominal CT scans were enrolled. All scans were obtained using a 320-slice wide-detector CT scanner released in March 2025. According to the scanning dose, patients were divided into a routine dose (RD) group (120 kV, 350 mA), a low dose 1 (LD1) group (120 kV, 175 mA), and a low dose 2 (LD2) group (120 kV, 70 mA). Iterative reconstruction algorithm (CV40%) was utilized in the RD group, while the LD1 and LD2 groups adopted CV40% and deep learning reconstruction algorithms (strength levels: 20%, 40%, 60%, and 80%). Quality evaluation was conducted on 99 image sets. The CT values and standard deviation (SD) of the liver and kidneys were measured, and the signal-to-noise ratio (SNR) was calculated. A 5-point scale was used to evaluate the noise level and sharpness of all images, and a linear mixed models analysis was conducted using SPSS software for objective assessment. Post hoc pairwise comparisons were corrected with the Bonferroni method. Results (1) Compared with the CTDIvol of the RD group, the radiation doses of LD1 and LD2 groups were reduced by 50% and 80%, respectively. (2) Compared with the RD_CV40%, the SD values of the LD1_CV40% and LD2_CV40% increased by 24% and 71%, while their SNR values decreased by 21% and 43%, respectively. (3) Intragroup comparisons of the LD1 and LD2 groups revealed that the SD values of the liver and kidneys gradually decreased and SNR values increased significantly at CI40%, CI60% and CI80% relative to CV40% (P<0.05). (4) No statistically significant differences were observed in the SD and SNR values of the liver and kidneys between LD1_CI40% and RD_CV40% (P>0.05). (5) Compared with RD_CV40%, the CI60% and CI80% in the LD1 and LD2 groups had lower SD values and higher SNR values in the liver and kidneys, with statistically significant differences (P<0.05). (6) Subjective evaluation showed that the noise and sharpness scores (score ≥3) of CI40%, CI60% and CI80% in the LD1 and LD2 groups were remarkably superior to those of CV40%, and the differences were statistically significant (P<0.05). Conclusion?hen the radiation dose for upper abdominal CT scanning is reduced by 50% and 80% relative to the conventional dose, the images reconstructed by deep learning reconstruction algorithm at strength levels ≥ 40% can achieve equivalent or even better image quality than conventional-dose images reconstructed by the RD_CV40% iterative algorithm. The proposed method can significantly reduce radiation exposure risks in clinical practice.
Last Update: 2026-07-22