ABSTRACT
A mathematical model of competition-type lateral flow immunoassay (LFIA) was developed to describe the dynamic process of LFIA.The competition-type LFIA was divided into two categories: TwA-competition-type LFIA and TnA-competition-type LFIA.On the basis of the developed model, the COMSOL software was exploited to simulate the dynamic process of LFIA.The simulation result demonstrated the relationships between the concentrations of substances on the test and control lines and the influence factors.In particular, the influence factors in the TwA-competition-type LFIA included the concentrations of target analyte A (0-20 mol/L) and reporter particle P (0.01-100 mol/L), and the position of the test line (5-20 mm).On the other hand, the influence factors in the TnA-competition-type LFIA included the concentrations of target analyte A (0-20 mol/L) and reporter particle P (0.01-100 mol/L), and the porosity.Experiment result showed that the developed model could be used to explore the influence of the parameters on the test results, and optimize the performance of LFIA.
ABSTRACT
Objective:Due to the inherent characteristics of medical ultrasound images, the study of B-type ultrasonic medical image filtering technique, you can make the image quality greatly improved.Methods: through tools such as Matlab, for b-mode ultrasonic image Adaptive mean filter, median filter, median filter, wavelet transform, anisotropic diffusion equation filtering;Results: compares the common b-mode ultrasonic filtering algorithms and research.Conclusion:all the filtering method for image signal is enhanced to a certain extent, suppression for noise, too. Mean square error reflects filtering image noise, while attention to detail, keep, for medical ultrasonic images in the hope that is in between the two to achieve an optimal balance.
ABSTRACT
In this paper, Acousto-Optical tomography is modeled by a linear integral equation and an inverse problem involving a diffusion equation in n-spatial dimensions. We make two-step mathematical model. First, we solve a linear integral equation. Assuming the optical energy fluence rate has been recovered from the previous equation, the absorption coefficient micro is then reconstructed by solving an inverse problem. Numerical experiments are presented for the case n=2. The traditional gradient descent method is used for the numerical simulations. The result of the gradient descent method produces the blurring effect. To get rid of the blurring effect, we suggest the total variation regularization for the minimization problem.