摘要
目的:探讨深度学习重建算法(DLIR)对头颈部CTA图像质量的影响。方法:回顾性收集50例头颈部CTA图像,分别进行DLIR-H、DLIR-M、DLIR-L和ASiR-V50%重建。测量并计算四组图像血管的背景噪声(SD)、血管锐利度(ERS)以及各重要层面血管的信噪比(SNR)和对比噪声比(CNR),并对四组图像进行主观质量评分。结果:随着DLIR强度增加,血管SD值显著降低(P<0.05),四组图像的ERS无统计学差异(P>0.05)。除大脑中动脉M1段外,其他血管SNR均有统计学差异(P<0.05),表现为DLIR-H最高,DLIR-M次之。所有血管CNR均有统计学差异(P<0.05),由高到低依次为DLIR-H,DLIR-M,DLIR-L和ASiR-V50%。四组图像主观质量评分无统计学差异(P>0.05)。结论:与ASIR-V50%相比,DLIR-H可以显著提高头颈部CTA图像质量,为优化头颈部CTA扫描方案创造条件。
Objective:To evaluate the impact of deep learning image reconstruction algorithm on the image quality in head and neck CT angiography.Methods:A total of 50 patients were retrospectively collected,all of the images were reconstructed by DLIR-H,DLIR-M,DLIR-L and ASiR-V50%.The background noise(standard deviation,SD),the edge rise slope(ERS),the signal-to-noise ratio(SNR),the contrast-to-noise ratio(CNR)were calculated and compared.Subjective evaluation of the four sets of images was also assessed by two radiologists.Results:As the strength of DLIR increased,the SD of vessels at each measurement level significantly decreased(P<0.05).There was no statistically significant difference in ERS among the four groups of images(P>0.05).The SNR of vessels showed statistical differences(P<0.05),with DLIR-H being the highest,followed by DLIR-M(except for MCA-M1).The CNR of all vessels showed statistical differences(P<0.05),with DLIR-H,DLIR-M,DLIR-L,and ASiR-V 50%in descending order.There was no statistically significant difference in subjective quality scores among the four groups of images(P>0.05).Conclusion:Compared with ASiR-V 50%,DLIR-H can significantly improve the image quality in head and neck CT angiography and optimize head and neck CT angiography protocols.
作者
林优优
张秋爽
潘璟琍
丁建荣
Lin Youyou;Zhang Qiushuang;Pan Jingli;Ding Jianrong(Department of Radiology,Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University,Taizhou,Zhejiang 317000;Department of Radiology,Enze Hospital,Taizhou Enze Medical Center(Group),Taizhou,Zhejiang 318050;Key Laboratory of Evidence-Based Radiology of Taizhou,Linhai,Zhejiang 317000)
出处
《现代医用影像学》
2024年第6期1171-1178,共8页
Modern Medical Imageology
基金
基于多模态磁共振探究PTPN6在颈动脉易损斑块作用机理并利用神经网络构建相关风险模型(省级),编号:13762022KY1376。
关键词
CT血管成像
深度学习重建算法
图像质量
CT angiography
deep learning image reconstruction algorithm
image quality
