Design and implementation of an automatic analysis system for magnetic resonance quality detection based on QT_Journal of Biomedical Engineering

Authors：

LUO Hongyan ^1,2 , XU Xu ^1,2 , GAO Chenglong ^1,2 , LI Mingyong ^1,2 ,  LIAO Yanjian ^1,2

1. Key Laboratory of Bio-Rheology Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing 400044, P.R.China;
2. Chongqing Medical Electronic Engineering Technology Research Center, Bioengineering College, Chongqing University, Chongqing 400044, P.R.China;

Corresponding author：

LIAO Yanjian, Email: azurelyj@163.com

Keywords：

magnetic resonance; quality inspection; image processing; automatic analysis; QT

DOI：

10.7507/1001-5515.201807014

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

The quality inspection of magnetic resonance imaging (MRI) performance parameters is an important means to ensure the image quality and the reliability of diagnosis results. There are some problems in the manual calculation and eye recognition of the quality inspection parameters, such as strong subjectivity and low efficiency. In view of these facts, an automatic analysis system for MRI quality detection based on QT is proposed and implemented in C++ language. The image processing algorithm is introduced to automatically measure and calculate the quality inspection parameters. The software with comprehensive functions is designed to systematically manage the quality inspection information of MRI. The experimental results show that the automatically calculated parameters are consistent with the manually calculated ones. Accordingly, the accuracy and reliability of the algorithm is verified. The whole system is efficient, convenient and easy to operate, and it can meet the actual needs of MRI quality inspection.

Citation： LUO Hongyan, XU Xu, GAO Chenglong, LI Mingyong, LIAO Yanjian. Design and implementation of an automatic analysis system for magnetic resonance quality detection based on QT. Journal of Biomedical Engineering, 2019, 36(4): 627-632. doi: 10.7507/1001-5515.201807014 Copy

1.	陈自谦. 影像医学在临床医学中的作用与价值[J/CD]. 功能与分子医学影像学(电子版), 2013, 2(1): 1-5. http://jmfmi.com/UploadFiles/2014-06/fzzyy315/2014062611291638436.pdf.
2.	朱迪. 影像医学在临床医学中的应用和价值. 中国社区医师, 2014, 30(35): 123, 125.
3.	Di Nallo A M, Ortenzia O, D'arienzo M, et al. MRI quality control tools for procedures and analyses. J Exp Clin Canc Res, 2006, 25(1): 121-127.
4.	邹广东, 郭淑宁. 关于MRI各项技术参数质量控制的分析研究及发展趋向. 世界最新医学信息文摘, 2015, 15(24): 29-30, 31.
5.	卫生部. 大型医用设备配置与应用管理暂行办法. 中国卫生法制, 1995(5): 37-38.
6.	YY/T 0482-2004. 医疗诊断用磁共振设备技术要求及试验方法. 北京: 国家食品药品监督管理局, 2004: 3-23.
7.	WS/T 263-2006. 医用磁共振成像(MRI)设备影像质量检测与评价规范. 北京: 人民卫生出版社, 2007: 6-17.
8.	Graham M S, Drobnjak I, Zhang Hui. A supervised learning approach for diffusion MRI quality control with minimal training data. NeuroImage, 2018, 178: 668-676.
9.	Golubev A, Bogatencov P, Secrieru G, et al. DICOM data processing optimization in medical information systems. Scalable Computing: Practice and Experience, 2018, 19(2): 189-201.
10.	Gonzalez R C, Woods R E. 数字图像处理. 第3版. 北京: 电子工业出版社, 2011: 479-483.
11.	杨建锋, 王重鸣. 类内相关系数的原理及其应用. 心理科学, 2008, 31(2): 434-437.
12.	杨奇明, 林坚. 组内相关系数: 定义辨析、估计方法与实际应用. 浙江大学学报: 理学版, 2013, 40(5): 509-515.
13.	余红梅, 罗艳虹, 萨建, 等. 组内相关系数及其软件实现. 中国卫生统计, 2011, 28(5): 497-500.
14.	潘晓平, 倪宗瓒. 组内相关系数在信度评价中的应用. 华西医科大学学报, 1999, 30(1): 62-63.
15.	李镒冲, 李晓松. 两种测量方法定量测量结果的一致性评价. 现代预防医学, 2007, 34(17): 3263-3266, 3269.

1. 陈自谦. 影像医学在临床医学中的作用与价值[J/CD]. 功能与分子医学影像学(电子版), 2013, 2(1): 1-5. http://jmfmi.com/UploadFiles/2014-06/fzzyy315/2014062611291638436.pdf.
2. 朱迪. 影像医学在临床医学中的应用和价值. 中国社区医师, 2014, 30(35): 123, 125.
3. Di Nallo A M, Ortenzia O, D'arienzo M, et al. MRI quality control tools for procedures and analyses. J Exp Clin Canc Res, 2006, 25(1): 121-127.
4. 邹广东, 郭淑宁. 关于MRI各项技术参数质量控制的分析研究及发展趋向. 世界最新医学信息文摘, 2015, 15(24): 29-30, 31.
5. 卫生部. 大型医用设备配置与应用管理暂行办法. 中国卫生法制, 1995(5): 37-38.
6. YY/T 0482-2004. 医疗诊断用磁共振设备技术要求及试验方法. 北京: 国家食品药品监督管理局, 2004: 3-23.
7. WS/T 263-2006. 医用磁共振成像(MRI)设备影像质量检测与评价规范. 北京: 人民卫生出版社, 2007: 6-17.
8. Graham M S, Drobnjak I, Zhang Hui. A supervised learning approach for diffusion MRI quality control with minimal training data. NeuroImage, 2018, 178: 668-676.
9. Golubev A, Bogatencov P, Secrieru G, et al. DICOM data processing optimization in medical information systems. Scalable Computing: Practice and Experience, 2018, 19(2): 189-201.
10. Gonzalez R C, Woods R E. 数字图像处理. 第3版. 北京: 电子工业出版社, 2011: 479-483.
11. 杨建锋, 王重鸣. 类内相关系数的原理及其应用. 心理科学, 2008, 31(2): 434-437.
12. 杨奇明, 林坚. 组内相关系数: 定义辨析、估计方法与实际应用. 浙江大学学报: 理学版, 2013, 40(5): 509-515.
13. 余红梅, 罗艳虹, 萨建, 等. 组内相关系数及其软件实现. 中国卫生统计, 2011, 28(5): 497-500.
14. 潘晓平, 倪宗瓒. 组内相关系数在信度评价中的应用. 华西医科大学学报, 1999, 30(1): 62-63.
15. 李镒冲, 李晓松. 两种测量方法定量测量结果的一致性评价. 现代预防医学, 2007, 34(17): 3263-3266, 3269.

Journal of Biomedical Engineering

Design and implementation of an automatic analysis system for magnetic resonance quality detection based on QT

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content