The research on cardiac volume-time relationship based on retrospective electrocardiograph four-dimension computer tomography data collection and structured sparse algorithm_Journal of Biomedical Engineering

Authors：

LI Meng ¹ , ZHAO Peng ¹ , XIAO Jingjing ¹ , QIAO Linbo ² , PAN Wencai ¹ , WANG Shuang ³ ,  CHONG Yinbao ¹

1. Department of Medical Engineering, The Second Affiliate Hospital of Army Medical University, Chongqing 400037, P.R.China;
2. College of Computer Science, National University of Defense Technology, Changsha 410073, P.R.China;
3. Department of Radiology, The Second Affiliate Hospital of Army Medical University, Chongqing 400037, P.R.China;

Corresponding author：

CHONG Yinbao, Email: chongyinbao@163.net

Keywords：

four-dimension computed tomography image; retrospective electrocardiograph gating; structured sparse algorithm; volume-time relationship

DOI：

10.7507/1001-5515.201710042

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

This paper explores the relationship between the cardiac volume and time, which is applied to control dynamic heart phantom. We selected 50 patients to collect their cardiac computed tomography angiography (CTA) images, which have 20 points in time series CTA images using retrospective electrocardiograph gating, and measure the volume of four chamber in 20-time points with cardiac function analysis software. Then we grouped patients by gender, age, weight, height, heartbeat, and utilize repeated measurement design to conduct statistical analyses. We proposed structured sparse learning to estimate the mathematic expression of cardiac volume variation. The research indicates that all patients’ groups are statistically significant in time factor (P = 0.000); there are interactive effects between time and gender groups in left ventricle (F = 8.597, P = 0.006) while no interactive effects in other chambers with the remaining groups; and the different weight groups’ volume is statistically significant in right ventricle (F = 9.004, P = 0.005) while no statistical significance in other chambers with remaining groups. The accuracy of cardiac volume and time relationship utilizing structured sparse learning is close to the least square method, but the former’s expression is more concise and more robust. The number of nonzero basic function of the structured sparse model is just 2.2 percent of that of least square model. Hence, the work provides more the accurate and concise expression of the cardiac for cardiac motion simulation.

Citation： LI Meng, ZHAO Peng, XIAO Jingjing, QIAO Linbo, PAN Wencai, WANG Shuang, CHONG Yinbao. The research on cardiac volume-time relationship based on retrospective electrocardiograph four-dimension computer tomography data collection and structured sparse algorithm. Journal of Biomedical Engineering, 2018, 35(2): 219-228. doi: 10.7507/1001-5515.201710042 Copy

1.	Lee Y J, Lim Y S, Lim H W, et al. Diagnostic accuracy of 64-slice multidetector CT angiography for detection of in-stent restenosis of vertebral artery ostium stents: comparison with conventional angiography. Acta radiol, 2014, 55(8): 1000-1007.
2.	Andreini D, Pontone G, Mushtaq S, et al. Diagnostic accuracy of multidetector computed tomography coronary angiography in 325 consecutive patients referred for transcatheter aortic valve replacement. Am Heart J, 2014, 168(3): 332-339.
3.	Lee A M, Beaudoin J, Engel L C, et al. Assessment of image quality and radiation dose of prospectively ECG-triggered adaptive dual-source coronary computed tomography angiography (cCTA) with arrhythmia rejection algorithm in systole versus diastole: a retrospective cohort study. Int J Cardiovasc Imaging, 2013, 29(6): 1361-1370.
4.	Beitzke D, Berger-Kulemann V, Schoepf V, et al. Dual-source cardiac computed tomography angiography (CCTA) in the follow-up of cardiac transplant: comparison of image quality and radiation dose using three different imaging protocols. Eur Radiol, 2015, 25(8): 2310-2317.
5.	Begemann P G C, Stevendaal U V, Manzke R, et al. Evaluation of spatial and temporal resolution for ECG-gated 16-row multidetector CT using a dynamic phantom. EurRadiol, 2005, 15: 1015-1026.
6.	赵鹏, 郎朗, 潘文才, 等. 基于动态心脏体模的冠脉 CTA 成像后处理软件质量评估//中国医学装备协会第二十五届学术年会, 贵阳: 中国医学装备协会, 2016: 58-62.
7.	Bassingthwaighte J B, Design and strategy for the cardionome project. Analytical and Quantitative Cardiology, 1997, 430: 325-339.
8.	张璞, 万国庆, 孙劼. 简述动态心脏模体在医学成像中的应用//全国射线数字成像技术研讨会论文集, 厦门: 中国体视学学会CT理论与应用分会, 2014: 211-216.
9.	Wood P W, Gibson P H, Becher H. Three-dimensional echocardiography in a dynamic heart phantom: comparison of five different methods to measure chamber volume using a commercially available software. Echo Research and Practice, 2014, 1(2): 51-60.
10.	陈圆圆. 新型心脏动态体模的设计与临床应用, 泰山: 泰山医学院, 2009.
11.	Horiguchi J, Shen Y, Akiyama Y, et al. Electron beam CT versus 16-MDCT on the variability of repeated coronary artery calcium measurements in a variable heart rate phantom. American Journal of Roentgenology, 2005, 185(4): 995-1000.
12.	葛夕洪, 廖静敏, 吴胜勇, 等. 心率不稳定时 3 种不同重建算法对影像质量的影响: MSCT 动态心脏体模研究. 实用放射学杂志, 2007, 23(6): 831-834.
13.	Zhen Xiantong, Zhang Heye, Islam A, et al. Direct and simultaneous estimation of cardiac four chamber volumes by multioutput sparse regression. Med Image Anal, 2017, 36: 184-196.
14.	徐跃, 梁碧玲, 赵强, 等. 医学影像设备学, 译. 第 3 版. 北京: 人民卫生出版社, 2010: 122-135.
15.	Husmann L, Leschka S, Desbiolles L, et al. Heart rate-implications for CT image reconstruction. Radiology, 2007, 245(2): 567-576.
16.	Qiao Linbo, Zhang Bofeng, Su Jinshu, et al. A systematic review of structured sparse learning. Frontiers of Information Technology & Electronic Engineering, 2017, 18(4): 445-463.
17.	毕超, 冯玉田, 李园辉, 等. 语音信号的分块稀疏表示分类研究. 计算机技术与发展, 2017, 3: 44-47.
18.	Xiao J J, Stolkin R, Gao Y, et al. Robust fusion of color and depth data for RGB-D target tracking using adaptive range-invariant depth models and spatio-temporal consistency constraints. IEEE Transactions on Cybernetics, 2017, 99: 1-15.
19.	Jiang B, Ma S Q, Causey J, et al. SparRec: an effective matrix completion framework of missing data imputation for GWAS, Scientific Reports, 2016, 9: 35534.
20.	Fan J Q, Li R Z. Variable selection via nonconcave penalized likelihood and its oracle properties. J Am Stat Assoc, 2001, 96(456): 1348-1360.
21.	Lin Tianyi, Ma Shiqian, Zhang Shuzhong. On the global linear convergence of the admm with multiblock variables. SIAM Journal on Optimization, 2015, 25(3): 1478-1497.
22.	Kim S, Chang Yongjin, Ra J B. Cardiac motion correction based on partial angle reconstructed images in x-ray CT. Med Phys, 2015, 42(5): 2560-2571.
23.	Aghayev A, Murphy D J, Keraliya A R. Recent developments in the use of computed tomography scanners in coronary artery imaging. Expert Rev Med Devices, 2016, 13(6): 545-553.
24.	Yamamoto S, Hamada S, Miyamoto M, et al. A new approach towards volumetric assessment of left ventricular function with MSCT. Biomed Imaging Interv J, 2006, 2(3): e50.

1. Lee Y J, Lim Y S, Lim H W, et al. Diagnostic accuracy of 64-slice multidetector CT angiography for detection of in-stent restenosis of vertebral artery ostium stents: comparison with conventional angiography. Acta radiol, 2014, 55(8): 1000-1007.
2. Andreini D, Pontone G, Mushtaq S, et al. Diagnostic accuracy of multidetector computed tomography coronary angiography in 325 consecutive patients referred for transcatheter aortic valve replacement. Am Heart J, 2014, 168(3): 332-339.
3. Lee A M, Beaudoin J, Engel L C, et al. Assessment of image quality and radiation dose of prospectively ECG-triggered adaptive dual-source coronary computed tomography angiography (cCTA) with arrhythmia rejection algorithm in systole versus diastole: a retrospective cohort study. Int J Cardiovasc Imaging, 2013, 29(6): 1361-1370.
4. Beitzke D, Berger-Kulemann V, Schoepf V, et al. Dual-source cardiac computed tomography angiography (CCTA) in the follow-up of cardiac transplant: comparison of image quality and radiation dose using three different imaging protocols. Eur Radiol, 2015, 25(8): 2310-2317.
5. Begemann P G C, Stevendaal U V, Manzke R, et al. Evaluation of spatial and temporal resolution for ECG-gated 16-row multidetector CT using a dynamic phantom. EurRadiol, 2005, 15: 1015-1026.
6. 赵鹏, 郎朗, 潘文才, 等. 基于动态心脏体模的冠脉 CTA 成像后处理软件质量评估//中国医学装备协会第二十五届学术年会, 贵阳: 中国医学装备协会, 2016: 58-62.
7. Bassingthwaighte J B, Design and strategy for the cardionome project. Analytical and Quantitative Cardiology, 1997, 430: 325-339.
8. 张璞, 万国庆, 孙劼. 简述动态心脏模体在医学成像中的应用//全国射线数字成像技术研讨会论文集, 厦门: 中国体视学学会CT理论与应用分会, 2014: 211-216.
9. Wood P W, Gibson P H, Becher H. Three-dimensional echocardiography in a dynamic heart phantom: comparison of five different methods to measure chamber volume using a commercially available software. Echo Research and Practice, 2014, 1(2): 51-60.
10. 陈圆圆. 新型心脏动态体模的设计与临床应用, 泰山: 泰山医学院, 2009.
11. Horiguchi J, Shen Y, Akiyama Y, et al. Electron beam CT versus 16-MDCT on the variability of repeated coronary artery calcium measurements in a variable heart rate phantom. American Journal of Roentgenology, 2005, 185(4): 995-1000.
12. 葛夕洪, 廖静敏, 吴胜勇, 等. 心率不稳定时 3 种不同重建算法对影像质量的影响: MSCT 动态心脏体模研究. 实用放射学杂志, 2007, 23(6): 831-834.
13. Zhen Xiantong, Zhang Heye, Islam A, et al. Direct and simultaneous estimation of cardiac four chamber volumes by multioutput sparse regression. Med Image Anal, 2017, 36: 184-196.
14. 徐跃, 梁碧玲, 赵强, 等. 医学影像设备学, 译. 第 3 版. 北京: 人民卫生出版社, 2010: 122-135.
15. Husmann L, Leschka S, Desbiolles L, et al. Heart rate-implications for CT image reconstruction. Radiology, 2007, 245(2): 567-576.
16. Qiao Linbo, Zhang Bofeng, Su Jinshu, et al. A systematic review of structured sparse learning. Frontiers of Information Technology & Electronic Engineering, 2017, 18(4): 445-463.
17. 毕超, 冯玉田, 李园辉, 等. 语音信号的分块稀疏表示分类研究. 计算机技术与发展, 2017, 3: 44-47.
18. Xiao J J, Stolkin R, Gao Y, et al. Robust fusion of color and depth data for RGB-D target tracking using adaptive range-invariant depth models and spatio-temporal consistency constraints. IEEE Transactions on Cybernetics, 2017, 99: 1-15.
19. Jiang B, Ma S Q, Causey J, et al. SparRec: an effective matrix completion framework of missing data imputation for GWAS, Scientific Reports, 2016, 9: 35534.
20. Fan J Q, Li R Z. Variable selection via nonconcave penalized likelihood and its oracle properties. J Am Stat Assoc, 2001, 96(456): 1348-1360.
21. Lin Tianyi, Ma Shiqian, Zhang Shuzhong. On the global linear convergence of the admm with multiblock variables. SIAM Journal on Optimization, 2015, 25(3): 1478-1497.
22. Kim S, Chang Yongjin, Ra J B. Cardiac motion correction based on partial angle reconstructed images in x-ray CT. Med Phys, 2015, 42(5): 2560-2571.
23. Aghayev A, Murphy D J, Keraliya A R. Recent developments in the use of computed tomography scanners in coronary artery imaging. Expert Rev Med Devices, 2016, 13(6): 545-553.
24. Yamamoto S, Hamada S, Miyamoto M, et al. A new approach towards volumetric assessment of left ventricular function with MSCT. Biomed Imaging Interv J, 2006, 2(3): e50.

Previous Article
Numerical simulation of the effect of virtual stent release pose on the expansion results
Next Article
A novel method of optic disk segmentation based on visual saliency and rotary scanning

Journal of Biomedical Engineering

The research on cardiac volume-time relationship based on retrospective electrocardiograph four-dimension computer tomography data collection and structured sparse algorithm

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content