Influence of Deep Learning Reconstruction Algorithm with Different Mixed Weights on the
Accuracy of Quantitative Analysis of Lung Nodules in Low-Dose CT Scanning
DENG Lei, GUO Baobin, YAO Yue, YANG Quanxin, LI Xiaohui
Department of Medical Imaging, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an Shaanxi 710004, China
Abstract：Objective To explore the influence of deep learning reconstruction algorithm on quantitative analysis accuracy and
image quality of simulated pulmonary nodules. Methods Low-dose CT scanning of a simulated chest model with nine simulated
nodules was performed using GE Revolution CT. Filtered back projection (FBP), and different mixed weights deep learning image
reconstruction (DLIR) algorithms and adaptive statistical iterative reconstruction-Veo (ASIR-V) algorithms were used for image
reconstruction. All images were automatically measured by an AI-assisted diagnostic software, and the results were compared with
manual measurements. The signal to noise ratio (SNR) and contrast to noise ratio (CNR) were calculated. Results Different weights
and algorithm both had no effect on the manual measurement of nodule diameter (P>0.05); but DLIR algorithm might result in a
statistically significant deviation between the automatic measurement of nodules and the true value (P=0.01). The SNR and CNR
calculated by the DLIR algorithm were the highest, and DLIR-H had the best image quality (P<0.05). Conclusion Compared with
ASiR-V and FBP algorithms, DLIR algorithm could improve image quality significantly. The higher the mixed weight of DLIR, the
more obvious the improvement of image quality, and the different measurement methods will not affect this result.
邓蕾，郭宝斌，姚悦，杨全新，李晓会. 不同混合权重深度学习重建算法对低剂量CT扫描肺结节定量分析准确性的影响[J]. 中国医疗设备, 2022, 37(5): 90-94.
DENG Lei, GUO Baobin, YAO Yue, YANG Quanxin, LI Xiaohui. Influence of Deep Learning Reconstruction Algorithm with Different Mixed Weights on the
Accuracy of Quantitative Analysis of Lung Nodules in Low-Dose CT Scanning. China Medical Devices, 2022, 37(5): 90-94.
