The application of deep learning-based artificial intelligence algorithms combined with low-dose scanning protocols in chest CT

作者：Xiaojing Liu, Xiaoli Ning, Shen Gui, Tian Liao, Hongying Wu, Jinge Ma, Ziqiao Lei · 发表于：Quantitative Imaging in Medicine and Surgery · 年份：2025 · DOI：10.21037/qims-2025-685 · 被引用次数：1 · 研究领域：Radiation Dose and Imaging、Advanced X-ray and CT Imaging、Cardiac Imaging and Diagnostics

Background: Under low-dose scanning conditions, different reconstruction algorithms have varying effects on image quality. This study aimed to investigate the effects of the precise imaging (PI) deep-learning artificial intelligence (AI) algorithm combined with a low-dose scanning protocol on image quality and radiation dose in chest computed tomography (CT) scans. Methods: A retrospective analysis was conducted of 100 patients who underwent non-contrast chest CT scans at Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, using Philips Incisive CT between October and December 31, 2024. The patients were divided into two groups: the experimental group (n=50) and the control group (n=50). The experimental group was scanned using a low-dose protocol with a tube voltage of 100 kVp, and the images were reconstructed using the PI deep-learning AI algorithm at a high-intensity level (Group A) and conventional iDose iterative reconstruction (Group B) for both the mediastinal and lung window settings, respectively. The control group (Group C) was scanned using a conventional-dose protocol with a tube voltage of 120 kVp, and the images were reconstructed by iDose iterative reconstruction. Objective image quality metrics, including the mean CT value, standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of the regions of interest (ROIs) in the axial images, were measured and calculated. The statistical analysis was pe...