Study of Deep Learning-Based Image Reconstruction Algorithms in the Diagnosis of
Lower Limb Arterial Lesions Using CTA
CHEN Yun1, ZHU Yan1, WANG Yang2, ZHAO Tian1, LI Yuefeng1, CHEN Xingbing2
1. Department of Radiology, Affiliated Hospital of Jiangsu University, Zhenjiang Jiangsu 212001, China;
2. Department of Radiology, Gaoyou People’s Hospital, Yangzhou Jiangsu 225600, China
Abstract：Objective To explore the diagnostic value of deep learning based image reconstruction algorithms for computed
tomography angiography (CTA) of lower limb arterial lesions. Methods CTA examination data from 51 patients (65 lower extremity
arteries) with lower extremity arterial stenosis or occlusion who were treated in our hospital from June 2021 to February 2022 was
retrospective collected. Based on the deep learning image reconstruction (DLIR) algorithm and hybrid iterative reconstruction (HIR)
algorithm, CTA images were reconstructed separately, and the quality was evaluated using HIR as a reference. Two physicians
assessed the location and degree of vascular stenosis under different reconstruction algorithms and observed interobserver
consistency using Kappa test. Digital subtraction angiography was used as the “gold standard” to compare the performance
of HIR and DLIR in diagnosing moderate and severe stenosis of lower extremity arteries. Results Compared with the HIR
algorithm, the noise of image quality in DLIR algorithm was significantly reduced (ZSuperior knee artery=8.36, ZInfrapopliteal artery=9.46,
ZDorsalis pedis artery =7.19, P<0.001), and signal to noise ratio (ZSuperior knee artery=-7.32, ZInfrapopliteal artery=-7.91, ZDorsalis pedis artery= - 8 . 4 5 ,
P<0.001) and contrast to noise ratio was significantly improved (ZSuperior knee artery=-8.66, ZInfrapopliteal artery=-9.21, ZDorsalis pedis artery= -8.52,
P<0.001). Compared with the HIR method, images reconstructed based on DLIR showed significantly improved sensitivity
(72.2% vs. 94.4%) and specificity (78.7% vs. 95.7%) for severe stenosis of the inferior knee artery, specificity for moderate stenosis
in the dorsal foot artery (86.0% vs. 97.7%) and the sensitivity for severe stenosis (50.0% vs. 87.5%) (P<0.05). Conclusion DLIR
algorithm can effectively enhance the quality of CTA images of lower extremity arteries, leading to improved diagnostic efficiency.
陈芸1，朱彦1，王扬2，赵天1，李月峰1，陈兴兵2. 基于深度学习的图像重建算法在下肢动脉
病变CTA诊断中的研究[J]. 中国医疗设备, 2024, 39(3): 134-138.
CHEN Yun1, ZHU Yan1, WANG Yang2, ZHAO Tian1, LI Yuefeng1, CHEN Xingbing2. Study of Deep Learning-Based Image Reconstruction Algorithms in the Diagnosis of
Lower Limb Arterial Lesions Using CTA. China Medical Devices, 2024, 39(3): 134-138.
