The research on cardiac volume-time relationship based on retrospective electrocardiograph four-dimension computer tomography data collection and structured sparse algorithm.
10.7507/1001-5515.201710042
- Author:
Meng LI
1
;
Peng ZHAO
1
;
Jingjing XIAO
1
;
Linbo QIAO
2
;
Wencai PAN
1
;
Shuang WANG
3
;
Yinbao CHONG
4
Author Information
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.
4. Department of Medical Engineering, The Second Affiliate Hospital of Army Medical University, Chongqing 400037, P.R.China.chongyinbao@163.net.
- Publication Type:Journal Article
- Keywords:
four-dimension computed tomography image;
retrospective electrocardiograph gating;
structured sparse algorithm;
volume-time relationship
- From:
Journal of Biomedical Engineering
2018;35(2):219-228
- CountryChina
- Language:Chinese
-
Abstract:
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 ( = 0.000); there are interactive effects between time and gender groups in left ventricle ( = 8.597, = 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 ( = 9.004, = 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.
