基于深度学习迭代重建算法的肺结节评估效果分析
Performance analysis of deep learning-based iterative reconstruction algorithms in the evaluation of lung nodules
杨名昊 1韩义成 2刘洪武 2侯庆锋1
作者信息
- 1. 山东第一医科大学(山东省医学科学院)放射学院 山东 泰安 271016
- 2. 山东第一医科大学附属省立医院医学影像科 山东 济南 250021
- 折叠
摘要
目的 以联影uCT968 螺旋CT为例,分别利用深度学习迭代重建算法(artificial intelligence iterative recon-struction,AIIR)和滤波反投影重建算法(filtered back projection,FBP)重建图像,并应用肺结节评估软件分析AIIR对肺结节评估性能的影响.方法 选取山东第一医科大学附属省立医院进行胸部平扫检查的有肺磨玻璃结节患者 67 例,分析患者的首次CT图像,所有病例的原始图像均采用观察组AIIR算法和对照组FBP算法进行图像重建,对两种重建图像分别使用人工智能肺结节评估软件进行评估,比较两者的结节评估结果、信噪比(signal to noise ratio,SNR)和对比度噪声比(contrast noise ratio,CNR)并统计有效辐射剂量,评估图像质量对智能分析算法准确性的影响.结果 对其中 56 例体积小于 150 mm3 的磨玻璃结节分析结果显示:AIIR算法对磨玻璃结节的识别率优于FBP算法,差异有统计学意义(P<0.05);使用AIIR算法图像的SNR和CNR均优于FBP算法,差异有统计学意义(P<0.05).结论 临床应用中,选用AIIR算法有利于提高肺部磨玻璃结节的识别率.另外,AIIR算法的低剂量扫描可以降低医源性辐射剂量,进一步保障患者的健康安全,值得推广使用.
Abstract
Objective Using the United Imaging uCT968 Spiral CT as a reference,the artificial intelligence iterative recon-struction(AIIR)algorithm and Filtered Back Projection(FBP)algorithm were used to reconstruct the images,and the lung nod-ules evaluation software was used to analyze the influence of AIIR on the performance of lung nodule evaluation.Methods A retrospective selection of 67 patients with ground-glass nodules(GGNs)in their lungs was made.These patients underwent chest CT scans at the Shandong Provincial Hospital Affiliated to Shandong First Medical University.An analysis of these patients'ini-tial CT scans was conducted,with all original case images being reconstructed using both the AIIR(experimental group)and FBP(control group).These two sets of images were then evaluated using both the InferVISION and uAI lung nodule assessment software.Their evaluation results,SNR,and CNR were compared and their effective radiation doses were counted.We also as-sessed the impact of image quality on the accuracy of AI analysis.Results The automated analysis from the AI lung nodule as-sessment software revealed that AIIR algorithm significantly increased the detection rate of GGNs compared to the FBP.Addition-ally,images reconstructed via AIIR demonstrated a significant improvement in both SNR and CNR,in the meanwhile,reduced radiation dose and enhanced image quality.Conclusion In clinical application,the AIIR reconstruction algorithm is beneficial to improve the diagnosis rate of lung ground-glass nodules.Moreover,the low-dose scanning of the AIIR algorithm can further minimize the iatrogenic radiation dose,protecting patients.
关键词
迭代重建/肺结节/体层摄影术,X线计算机/深度学习Key words
Iterative Reconstruction/Lung nodules/Tomography,X-ray computed/Deep learning引用本文复制引用
基金项目
山东省医药卫生科技发展计划(202009040477)
出版年
2024
NETL
NSTL
万方数据