Abstract：Objective To explore the application value of large matrix combined with full iterative model reconstruction (IMR) in
artificial intelligence (AI) detection of lung nodules, and compare the effects of filter back projection (FBP), 1024×1024 iDose4,
and 1024×1024 IMR reconstruction algorithms on image quality. Methods A total of 60 patients with suspected pulmonary nodules
who required chest thin-layer CT were included in this study. After scanning, the original data of all patients were analyzed by routine
FBP, 1024×1024 iDose4, 11024×1024 IMR algorithm reconstruction. Image noise standard deviation (SD), signal to noise ratio
(SNR) and contrast to noise ratio (CNR) of three groups of reconstructed images were compared. The effects of the three groups
of images on the detection rate, true positive number and false positive rate of pulmonary nodules under the artificial intelligence
diagnosis system (CAD) were compared. Results The noise reduction ability of FBP, 1024×1024 iDose4 and 1024×1024 IMR
was improved in turn. The SNR and CNR of the three groups of images were improved in this order. With the improvement of
SNR and CNR, the spatial resolution of the images was also improved, and the ability of AI to identify pulmonary nodules
was improved. Compared with the conventional FBP group, the sensitivity of AI to identify lung nodules in the IMR group
was increased by 16.9%, and the false positive rate was reduced by 17.8%. Conclusion Large matrix combined with IMR
technology can improve the spatial resolution of image and improve the detection ability of ground glass nodules in the process of
CAD recognition of pulmonary nodules, which is worthy of clinical promotion.
王帅，李剑，李笑石，吴志斌，姜文龙，田健. 大矩阵联合不同重建方式对人工智能检出肺结节的影响[J]. 中国医疗设备, 2021, 36(10): 36-39.
WANG Shuai, LI Jian, LI Xiaoshi, WU Zhibin, JIANG Wenlong, TIAN Jian. Effects of Large Matrix Combined with Different Reconstruction Methods on AI Detection of
Pulmonary Nodules. China Medical Devices, 2021, 36(10): 36-39.
