Application value of combining low tube voltage and deep learning image reconstruction algorithm in head and neck CT angiography with reduced radiation and contrast doses.

Junjun Li¹, Yi Xiao², Le Cao¹, Yannan Cheng¹, Yanan Li¹, Ting Liang¹, Jianying Li³, Kang Huo⁴, Jianxin Guo⁵

¹Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.

²Ultrasonic Medicine Department, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.

³GE Healthcare, Computed Tomography Research Center, Beijing 100176, China.

⁴Department of Neurology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.

⁵Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China. Electronic address: gjx1665@xjtufh.edu.cn.

PubMed DOI

Abstract

OBJECTIVE: To demonstrate the value of combining low tube voltage (80kVp) and deep learning image reconstruction (DLIR) in head and neck CT angiography (CTA) with reduced radiation and contrast doses, in comparison with routine dose scans with 100kVp and adaptive statistical iterative reconstruction V (ASIR-V). METHODS: This prospective study included 100 patients scheduled for head and neck CTA…

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Mentioned Companies

GE HealthCare
affiliation

MeSH Terms

Humans0Computed Tomography Angiography*Radiation Dosage*Female0Contrast Media0Male0Radiographic Image Interpretation, Computer-Assisted*Algorithms0Deep Learning*Prospective Studies0Middle Aged0Aged0Neck*Adult0Head*Reproducibility of Results0Radiation Protection*Sensitivity and Specificity0

Keywords

Computed tomography angiographyDeep learning image reconstructionImage qualityRadiation dosageTriple-low technologies

Article Info

PMID: 42580075
DOI: 10.1016/j.ejrad.2026.113072
Received: May 11, 2026
Accepted: July 9, 2026

How to Cite

Li J et al. Application value of combining low tube voltage and deep learning image reconstruction algorithm in head and neck CT angiography with reduced radiation and contrast doses. Eur J Radiol2026;204113072. doi:10.1016/j.ejrad.2026.113072

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