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.
ABSTRACT
The real-time monitoring of cerebral hemorrhage can reduce its disability and fatality rates greatly. On the basis of magnetic induction phase shift, we in this study used filter and amplifier hardware module, NI-PXI data-acquisition system and LabVIEW software to set up an experiment system. We used Band-pass sample method and correlation phase demodulation algorithm in the system. In order to test and evaluate the performance of the system, we carried out saline simulation experiments of brain hemorrhage. We also carried out rabbit cerebral hemorrhage experiments. The results of both saline simulation and animal experiments suggested that our monitoring system had a high phase detection precision, and it needed only about 0.030 4s to finish a single phase shift measurement, and the change of phase shift was directly proportional to the volume of saline or blood. The experimental results were consistent with theory. As a result, this system has the ability of real-time monitoring the progression of cerebral hemorrhage precisely, with many distinguished features, such as low cost, high phase detection precision, high sensitivity of response so that it has showed a good application prospect.
Subject(s)
Animals , Rabbits , Algorithms , Cerebral Hemorrhage , Diagnosis , Computer Systems , Magnetics , SoftwareABSTRACT
This study was aimed to improve the sensitivity of magnetic induction phase shift detection system for cerebral hemorrhage. In the study, a cerebral hemorrhage model with 13 rabbits was established by injection of autologous blood and the cerebral hemorrhage was detected by utilizing magnetic induction phase shift spectroscopy (MIPSS) detection method under the feature band. Sixty five groups of phase shift spectroscopy data were obtained. According to the characteristics of cerebral hemorrhage phase shift spectroscopy under the feature hand, an effective method, B-F distribution, to diagnose the severity of cerebral hemorrhage was designed. The results showed that using MIPSS detection method under feature band, the phase shift obviously growed with increase of injection volume of autologous blood, and the phase shift induced by a 3-mL injection reached -7.750 3 degrees ± 1.420 4 degrees. B-F distribution could effectively diagnose the severity of cerebral hemorrhage. It can be concluded that the sensitivity of the cerebral hemorrhage magnetic induction detection system is improved by one order of magnitude with the MIPSS detection method under the feature band.