收藏切换
Application of ATCM and deep learning reconstruction in pediatric head CT: a study on image quality and radiation dose
收藏切换
Xijian Chen1a, b, Yan Sun1a, b, Jie Zhou2, Haiping Wang1a, b, Gang Ning1a, b, *
Modern Instruments & Medical Treatment | 2026, 32(2) : 7 - 14
Less
收藏切换
Modern Instruments & Medical Treatment | 2026, 32(2): 7-14
Application of ATCM and deep learning reconstruction in pediatric head CT: a study on image quality and radiation dose
Full
Xijian Chen1a, b, Yan Sun1a, b, Jie Zhou2, Haiping Wang1a, b, Gang Ning1a, b, *
Affiliations
  • 1a.Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, China
  • b.Key Laboratory of Birth Defects and Related Gynecological and Pediatric Diseases, Ministry of Education, Sichuan University, Chengdu 610041, China
  • 2.Neusoft Medical Systems Co., Ltd., Dalian 110167, China
Published: 2026-04-10 doi: 10.11876/mimt202602002
Outline
收藏切换
Objective

This study aimed to assess radiation dose reduction efficacy and image quality impact of automatic tube current modulation (ATCM) combined with deep-learning reconstruction algorithm “ClearInfinity” in low-dose pediatric non-contrast head CT scans.

Methods

This study retrospectively analyzed imaging data from 160 pediatric patients who underwent non-contrast head CT scans. The patients were divided into two groups according to the scanning protocol: group A (n=80) used the low-dose reconstruction protocol based on the ATCM technology combined with the ClearInfinity algorithm; Group B (n=80) adopted the conventional-dose reconstruction protocol using fixed parameters combined with iterative reconstruction. The patients were divided into four age groups, with 20 cases in each group. Radiation dose was evaluated using metrics such as volume CT dose index (CTDIvol), dose-length product (DLP), and effective dose (ED) to compare the radiation exposure levels between the two groups. Image quality assessment included both objective and subjective evaluations. For the objective evaluation, regions of interest (ROIs) with an area of approximately 20 mm2 were selected at four anatomical levels to calculate the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) for each region. Subjective evaluation was performed by two experienced physicians using a blinded method to rate the overall image quality, the degree of anatomical detail display, and the control of posterior cranial fossa artifacts.. Statistical analysis was conducted using SPSS software.

Results

The result shows that there was no statistically significant difference in the baseline data between the two groups. Radiation dose metrics, including CTDIvol, DLP, and ED, as well as objective image evaluation indicators such as SNR and CNR in different regions, all showed statistically significant differences (P<0.05). The radiation doses in Group A were all significantly lower than those in Group B. The subjective evaluation results indicated that the overall image quality of group A was improved compared to group B (P<0.001), the posterior cranial fossa artifacts were optimized (P<0.001), and the consistency of the evaluations by the two physicians was excellent (ICC=0.891~0.923, P<0.001).

Conclusion

The ATCM technology combined with the ClearInfinity algorithm can significantly reduce the radiation dose of pediatric head CT scans. Although the objective evaluation indicates a slight decline in image quality for older age groups, the subjective evaluation shows an improvement in image quality, achieving a coordinated optimization of dose and image quality, and is expected to provide a new paradigm for low-dose CT scans of the pediatric head.

Computed tomography (CT)  /  Pediatric  /  Deep learning reconstruction algorithm  /  Automatic tube current modulation  /  Radiation dose  /  Image quality  /  Low-dose scanning
陈锡建, 孙艳, 周杰, 王海平, 宁刚. 自动管电流调制技术联合深度学习重建算法应用于儿童头部CT图像质量及辐射剂量研究. 现代仪器与医疗, 2026 , 32 (2) : 7 -14 . DOI: 10.11876/mimt202602002
Xijian Chen, Yan Sun, Jie Zhou, Haiping Wang, Gang Ning. Application of ATCM and deep learning reconstruction in pediatric head CT: a study on image quality and radiation dose[J]. Modern Instruments & Medical Treatment, 2026 , 32 (2) : 7 -14 . DOI: 10.11876/mimt202602002
Year 2026 volume 32 Issue 2
62
11
Cite this Article
BibTeX
Article Info
doi: 10.11876/mimt202602002
  • Online Date:2026-10-23
  • Published:2026-04-10
Article Data
Affiliations
History
Funding
Affiliations
    1a.Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, China
    b.Key Laboratory of Birth Defects and Related Gynecological and Pediatric Diseases, Ministry of Education, Sichuan University, Chengdu 610041, China
    2.Neusoft Medical Systems Co., Ltd., Dalian 110167, China
References
Share
https://castjournals.cast.org.cn/joweb/xdyqyyl/EN/10.11876/mimt202602002
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT