深度学习重建算法联合智能去除金属伪影技术改善危重患者上腹部CT的图像质量
目的 评估基于深度学习算法联合智能去除金属伪影技术(deep learning combined with smart metal artifact reduction,DLMAR)对无法举起手臂且需要心电监护的危重患者上腹部CT图像质量的影响.方法 回顾性纳入无法举起手臂且需要心电监护的102例危重患者.对图像静脉期分别采用滤波反投影(filtered back projection,FBP)、迭代重建(iterative reconstruction,IR)、深度学习(deep learning,DL)、滤波反投影联合智能去除金属伪影技术(filtered back projection...
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| Published in | 四川大学学报（医学版） Vol. 55; no. 6; pp. 1403 - 1409 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
四川大学华西医院放射科华西磁共振研究中心(成都 610041)
20.11.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1672-173X |
| DOI | 10.12182/20241160102 |
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| Abstract | 目的 评估基于深度学习算法联合智能去除金属伪影技术(deep learning combined with smart metal artifact reduction,DLMAR)对无法举起手臂且需要心电监护的危重患者上腹部CT图像质量的影响.方法 回顾性纳入无法举起手臂且需要心电监护的102例危重患者.对图像静脉期分别采用滤波反投影(filtered back projection,FBP)、迭代重建(iterative reconstruction,IR)、深度学习(deep learning,DL)、滤波反投影联合智能去除金属伪影技术(filtered back projection combined with smart metal artifact reduction,FBPMAR)、自适应统计迭代重建联合智能去除金属伪影技术(adaptive statistical iterative reconstruction-V combined with smart metal artifact reduction,IRMAR)、DLMAR共6种算法重建图像.对肝脏无伪影区域、肝脏有金属伪影区域、两手臂间组织(肝、脾、胰、主动脉)的CT值、噪声、信噪比(signal-to-noise ratio,SNR)、对比度噪声比(contrast-to-noise ratio,CNR)进行定量分析.并采用5级评分法,对电极金属伪影、两手臂间结构的显示和图像噪声进行定性分析(1=最差,5=最佳).结果 在肝脏有金属伪影的区域:DLMAR组[(98.5±9.8)H[J]与FBP组[(73.7±5.6)H[J]、IR组[(75.3±7.5)H[J]、DL组[(66.3±11.4)H[J]的 CT值差异有统计学意义(P＜0.01);DLMAR与FBPMAR[(99.8±4.8)H[J]、IRMAR[(99.6±3.4)H[J]的CT值差异无统计学意义;DLMAR噪声均低于其他组(P＜0.01);DLMAR的SNR和CNR均高于其他组(P＜0.01).在两手臂间组织区域:6组的CT值差异无统计学意义;DLMAR噪声均低于其他组(P＜0.01);DLMAR的SNR和CNR均高于其他组(P＜0.01).FBPMAR、IRMAR、DLMAR组在去金属伪影方面的得分(4.27±0.32、4.44±0.34、4.61±0.28)均高于F |
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| AbstractList | 目的 评估基于深度学习算法联合智能去除金属伪影技术(deep learning combined with smart metal artifact reduction,DLMAR)对无法举起手臂且需要心电监护的危重患者上腹部CT图像质量的影响.方法 回顾性纳入无法举起手臂且需要心电监护的102例危重患者.对图像静脉期分别采用滤波反投影(filtered back projection,FBP)、迭代重建(iterative reconstruction,IR)、深度学习(deep learning,DL)、滤波反投影联合智能去除金属伪影技术(filtered back projection combined with smart metal artifact reduction,FBPMAR)、自适应统计迭代重建联合智能去除金属伪影技术(adaptive statistical iterative reconstruction-V combined with smart metal artifact reduction,IRMAR)、DLMAR共6种算法重建图像.对肝脏无伪影区域、肝脏有金属伪影区域、两手臂间组织(肝、脾、胰、主动脉)的CT值、噪声、信噪比(signal-to-noise ratio,SNR)、对比度噪声比(contrast-to-noise ratio,CNR)进行定量分析.并采用5级评分法,对电极金属伪影、两手臂间结构的显示和图像噪声进行定性分析(1=最差,5=最佳).结果 在肝脏有金属伪影的区域:DLMAR组[(98.5±9.8)H[J]与FBP组[(73.7±5.6)H[J]、IR组[(75.3±7.5)H[J]、DL组[(66.3±11.4)H[J]的 CT值差异有统计学意义(P＜0.01);DLMAR与FBPMAR[(99.8±4.8)H[J]、IRMAR[(99.6±3.4)H[J]的CT值差异无统计学意义;DLMAR噪声均低于其他组(P＜0.01);DLMAR的SNR和CNR均高于其他组(P＜0.01).在两手臂间组织区域:6组的CT值差异无统计学意义;DLMAR噪声均低于其他组(P＜0.01);DLMAR的SNR和CNR均高于其他组(P＜0.01).FBPMAR、IRMAR、DLMAR组在去金属伪影方面的得分(4.27±0.32、4.44±0.34、4.61±0.28)均高于F |
| Abstract_FL | Objective To evaluate the effect of deep learning reconstruction algorithm combined with smart metal artifact reduction(DLMAR)on the quality of abdominal CT images in critically ill patients who are unable to raise their arms and require electrocardiographic(ECG)monitoring.Methods A total of 102 patients were retrospectively enrolled.All subjects were critically ill patients who were unable to raise their arms and required ECG monitoring.Images were reconstructed using 6 algorithms,including filtered back projection(FBP),iterative reconstruction(IR),deep learning(DL),FBP combined with smart metal artifact reduction(FBPMAR),adaptive statistical iterative reconstruction-V combined with smart metal artifact reduction(IRMAR),and DLMAR.A quantitative analysis of CT values,noise,signal-to-noise ratio(SNR),and contrast-to-noise ratio(CNR)was conducted in regions without metal artifacts and regions with metal artifacts in the liver,as well as the tissues,including those from the liver,spleen,pancreas,and aorta,between the two arms.Qualitative analysis of electrode metal artifacts,the visualization of the structures between the two arms,and image noise was performed with a 5-point scoring system(l=worst and 5=best).Results In the regions of the liver with metal artifacts,there was a significant difference between the CT values of the DLMAR group([98.5±9.8]Hounsfield units[H[J])and those of the FBP group([73.7±5.6]HU),the IR group([75.3±7.5]HU),and the DL group([66.3±11.4]HU)(P＜0.01).There was no significant difference between the CT values of the DLMAR group and those of the FBPMAR group([99.8±4.8]HU)and the IRMAR group([99.6±3.4]HU)(P＞0.05).The noise of the DLMAR group was found to be significantly lower than that of the other groups(P＜0.01).Furthermore,the SNR and CNR of the DLMAR group were also found to be higher than those of the other groups(P＜0.01).In the tissue region between the two arms,the differences in CT values among the six groups were not statistically significant(P＞0.05).The noise of the DLMAR group was lower than those of the other groups(P＜0.01),and the SNR and CNR of the DLMAR group were higher than those of the other groups(P＜0.01).In terms of the removal of metal artifacts,the scores of the FBPMAR,IRMAR,and DLMAR groups(4.27±0.32,4.44±0.34,and 4.61±0.28,respectively)were higher than those of the FBP,IR,and DL groups(1.36±0.54,1.32±0.45,and 1.24±0.46,respectively)(P＜0.01).The DLMAR group also had a higher score of 4.62±0.37 in the visualization of structures between the two arms and 4.53±0.39 in the noise reduction of images,both of which were higher than those of the other groups(P＜0.01).Conclusion DLMAR reduces artifacts,decreases noise,and improves the quality of abdominal CT imaging in critically ill patients who are unable to raise their arms and require ECG monitoring. |
| Author | 姚小玲 孙怀强 夏春潮 谢薇 李真林 高荣慧 潘云龙 |
| AuthorAffiliation | 四川大学华西医院放射科华西磁共振研究中心(成都 610041) |
| AuthorAffiliation_xml | – name: 四川大学华西医院放射科华西磁共振研究中心(成都 610041) |
| Author_FL | PAN Yunlong XIA Chunchao GAO Ronghui LI Zhenlin SUN Huaiqiang YAO Xiaoling XIE Wei |
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| DocumentTitle_FL | Deep Learning Reconstruction Algorithm Combined With Smart Metal Artifact Reduction Technique Improves Image Quality of Upper Abdominal CT in Critically Ill Patients |
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| Keywords | Deep learning Metal artifact 危重患者 深度学习 Critically ill patients 金属伪影 腹部计算机断层扫描 Abdominal CT |
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| Title | 深度学习重建算法联合智能去除金属伪影技术改善危重患者上腹部CT的图像质量 |
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