[1]李彩霞,王建平,齐宏亮,等.双神经网络深度学习算法对不同低剂量上腹部CT图像质量的评估[J].中国医学物理学杂志,2026,43(7):905-909.[doi:DOI:10.3969/j.issn.1005-202X.2026.07.010]
　LI Caixia? WANG Jianping,QI Hongliang,HUANG Meiyan,et al.Evaluation of upper abdominal CT image quality at various low radiation doses using dual-neural-network deep learning algorithm[J].Chinese Journal of Medical Physics,2026,43(7):905-909.[doi:DOI:10.3969/j.issn.1005-202X.2026.07.010]
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双神经网络深度学习算法对不同低剂量上腹部CT图像质量的评估()

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
43卷
期数:
2026年第7期
页码:
905-909
栏目:
医学影像物理
出版日期:
2026-07-22

文章信息/Info

Title:
Evaluation of upper abdominal CT image quality at various low radiation doses using dual-neural-network deep learning algorithm
文章编号:
1005-202X（2026）07-0905-05
作者:
李彩霞1; 王建平1; 齐宏亮2; 黄美燕3; 李典育1; 周建伟1
1.南方医科大学南方医院影像诊断科， 广东 广州 510515； 2.南方医科大学南方医院医学工程科， 广东 广州510515； 3.南方医科大学生物医学工程学院， 广东 广州510515
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
关键词:
CT; 宽体探测器; 深度学习重建算法; 低剂量; 图像质量
Keywords:
Keywords: computed tomography wide-detector deep learning reconstruction algorithm low-dose image quality
分类号:
R318；R811.1
DOI:
DOI:10.3969/j.issn.1005-202X.2026.07.010
文献标志码:
A
摘要:
目的：探讨国产宽体超高分辨率CT结合双神经网络深度学习重建技术（DLIR-CI）对上腹部不同辐射剂量方案CT图像质量的优化作用。方法：纳入33例行上腹部CT平扫的患者，采用2025年3月上市的320排宽体探测器CT进行扫描，根据扫描剂量的不同分为常规剂量（RD）组（120 kV, 350 mA）、低剂量1（LD1）组（120 kV, 175 mA）和低剂量2（LD2）组（120 kV, 70 mA）。所有RD组采用迭代重建算法（CV40%）重建，LD1组和LD2组均采用CV40%和深度学习重建算法（CI）（强度系数为20%/40%/60%/80%）重建，总共对99层图像进行质量评估，测量不同算法肝脏和肾脏的CT值及标准差（SD），并计算信噪比（SNR）。同时采用5分评价法评价所有图像的噪声等级和锐利度，客观评价采用SPSS软件的线性混合模型分析，事后两两比较采用Bonferroni法校正。结果：（1）与RD组的CTDIvol相比，LD1组和LD2组的剂量分别降低50%和80%；（2）与RD_CV40%相比，LD1_CV40%和LD2_CV40%的SD值分别增加24%和71%，SNR值分别降低21%和43%；（3）在LD1组和LD2组的组内比较中，CI40%、CI60%、CI80%的SD值和SNR值分别与CV40%相比，肝脏和肾脏的SD值均逐渐降低，SNR值均显著提高（P<0.05）；（4）LD1_CI40%与RD_CV40%相比，肝脏和肾脏的SD值和SNR值差异均无统计学意义（P>0.05）；（5）LD1组和LD2组的CI60%和CI80%分别与RD_CV40%相比，肝脏和肾脏的SD值更低，SNR值更高，差异均有统计学意义（P<0.05）；（6）主观评价中，LD1组和LD2组的CI40%、CI60%和CI80%的噪声等级评分和锐利度评分均明显优于RD_CV40%的评分，差异有统计学意义（P<0.05）。结论：上腹部CT扫描剂量较常规剂量降低50%、80%时，结合深度学习重建算法（强度系数≥40%）的图像可以达到常规剂量迭代重建CV40%的效果，甚至更优的图像质量，在临床应用中明显降低辐射风险。
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.

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备注/Memo

备注/Memo:
【收稿日期】2026-02-12 【基金项目】广东省自然科学基金（2024A1515012023）；南方医科大学南方医院院长基金（2024A009） 【作者简介】李彩霞，硕士，研究方向：CT成像技术及应用，E-mail: caixiali0329@163.com 【通信作者】周建伟，主管技师，研究方向：医学影像成像技术，E-mail: 1156959311@qq.com
更新日期/Last Update: 2026-07-22