Education, Science, Technology, Innovation and Life
Open Access
Sign In

Summary of the methods of restraining the decline of evaluation accuracy on the degree of coronary stenosis caused by coronary artery calcification at the present stage

Download as PDF

DOI: 10.23977/phpm.2021.010107 | Downloads: 3 | Views: 441

Author(s)

Zhiyi Chen 1

Affiliation(s)

1 School of Biomedical Engineering, Southern Medical University, Guangzhou, China, 510515

Corresponding Author

Zhiyi Chen

ABSTRACT

Coronary computed tomography angiography (CCTA), as a non-invasive method to detect the degree of coronary artery stenosis, is often used as a primary screening method for asymptomatic patients, but the performance in positive predictive value often fluctuates. The main reason is the partial volume effect caused by large area coronary artery calcification plaque, which limits the clinical application of CCTA. This article summarizes the methods of suppressing evaluation errors on the degree of stenosis caused by coronary artery calcification, in order to provide some reference for clinical workers in the treatment of patients with severe calcification.

KEYWORDS

Coronary computed tomography angiography (CCTA), coronary artery calcification plaque, false positive, decreased accuracy

CITE THIS PAPER

Zhiyi Chen. Summary of the methods of restraining the decline of evaluation accuracy on the degree of coronary stenosis caused by coronary artery calcification at the present stage. MEDS Public Health and Preventive Medicine (2021) 1: 41-44. DOI: http://dx.doi.org/10.23977/phpm.2021.010107.

REFERENCES

[1] Hu Shengshou et al. “Summary of Chinese Cardiovascular Disease report 2018”, Chinese Journal of Circulation, 2019, 34(3): 209-220.
[2] Zhang Hongjun, “The value of Coronary artery CT Angiography in the diagnosis of Coronary Heart Disease”, Journal of chronic Diseases, 2021, 22(4): 558-559, 2021.
[3] Xu Weihua, “Multi-slice spiral CT angiography Evaluation of coronary stenosis and plaque stability of coronary atherosclerotic heart disease”, imaging science and photochemical, 2020, 38(3): 491-495.
[4] Wu Jun, “Influence of Calculated Fabuncture in Calculated Campaign in Calcular CT”, Imaging Research and Medical Applications, 2017, 1(15): 76-77.
[5] Wen Yun, Zeng Wenbing, Li Jianrong, Wang Jing and Li Xiang, “Study on the consistency of Revolution CT Coronary Angiography and Coronary Angiography in the Evaluation of Coronary artery Stenosis”, Western Medicine, 2019,31(8):1278-1282.
[6] Chen Weibin, Feng Li, Zhang Weijie, Gong Fengling, Wang Xingcon and Zhang Huiying, “Comparison Different Injection Schemes on Coronary CTA Image Quality”, Medical Research Magazine, 2017, 46(5): 171-174+3.
[7] “Application of Magic Mirror in CT Angiography of Coronary artery”, wanfangdata.com.cn.
[8] Shen Junlin, Du Xiangying and Li Kun Cheng, “Progress in Ittere Reconstruction Technology and Its Application in Coronary CT Vascular Imaging”, International Medical Radiology, 2012, 35(6): 562-565.
[9] M. Renker, et al, “Evaluation of Heavily Calcified Vessels with Coronary CT Angiography: Comparison of Iterative and Filtered Back Projection Image Reconstruction”, Radiology, 2011 260(2): 390–399.
[10] R. Tanaka et al, “Improved evaluation of calcified segments on coronary CT angiography: a feasibility study of coronary calcium subtraction”, Int J Cardiovasc Imaging, 2013, 29(S2): 75–81.
[11] M. Amanuma et al, “Subtraction coronary computed tomography in patients with severe calcification”, Int J Cardiovasc Imaging, 2015, 31(8): 1635–1642.
[12] Liu Zinuan, Yang Junjie and Chen Yundai, Application and Progress of Machine Learning in Coronary artery computed Tomography, Medical Journal of the people's Liberation Army, 2021, 46(3):286-293.
[13] F. Tatsugami et al, “Deep learning–based image restoration algorithm for coronary CT angiography”, Eur Radiol, 2019, 29(10): 5322–5329.
[14] T. Lossau (née Elss), H. Nickisch, T. Wissel, M. Morlock and M. Grass, “Learning metal artifact reduction in cardiac CT images with moving pacemakers”, Medical Image Analysis, 2020, 61:101655.
[15] H. S. Park, S. M. Lee, H. P. Kim and J. K. Seo, “CT sinogram-consistency learning for metal-induced beam hardening correction” , Med. Phys., 2018, 45(12): 5376–5384.
[16] Le Guanming, Qiu Sihuang, “SPECT Myocardial Imaging combined with Coronary CT in the diagnosis of Coronary Stenosis”, Journal of Clinical Electrocardiology, 2020, 29(3): 33-35.
[17] Zhuang Lingling and Zhang Wei, “Analysis of the Factors Analysis of Dual Source CT Coronary Mechanization Assessment Coronary Striosis”, China Medical Computer Imaging Magazine, 2015, 21(6): 601-604.

Downloads: 151
Visits: 10756

Sponsors, Associates, and Links


All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.