IRANIAN JOURNAL OF BIOMEDICAL ENGINEERING
Year:2005 | Volume:2 | Issue:1|Papers Count:8

Using ceramic coatings has increased in popularity due to their compatibility with bone, absence of the fibrous layer at the coating-implant interface, and the stronger coating-bone bonding. Among these coatings, hydroxyapatite (HA) and fluoroapatite (FA) are more popular. For the first time in this paper, modeling and stress analysis have been carried out for 24 implants in an axisymetric form using the finite element technique. Twelve of these samples belong to IMZ and the rest are from Dyna system. All implants had HA and FA coatings with thicknesses between 10 to 100 microns. The stress analysis results show that the stress concentration at the implant-coating and bone-coating bonding surfaces decreases with the increase of coating thickness. In addition, stress concentrations for implants with FA coatings are always more than those with HA coatings. In all implants, stress concentration has been observed around the bone crest.

A mathematical model is presented for simulation of neurotransmitter release in the synaptic cleft of a neuromuscular junction. Chaudhuri's model is improved by adding calcium diffusion effect on the neurotransmitter release. When an action potential occurs, the calcium channels on presynaptic membrane will open and allow calcium ions to enter in presynaptic terminal. Then, these ions diffuse between calcium channels and release sites, where clearance mechanisms remove some of them. The model is defined by some partial differential equations which are solved by numerical methods. Solving these equations, the temporal changes of calcium concentration in the release sites and the amount of neurotransmitter release at each time are obtained. Finally, the effect of two consecutive action potential pulses on the above mechanisms is studied.

Based on a discrete dynamic contour model, a method for segmentation of brain structures like thalamus and red nucleus from magnetic resonance images (MRI) is developed. A new method for solving common problems in extracting the discontinuous boundary of a structure from a low contrast image is presented. External and internal forces deform the dynamic contour model. Internal forces are obtained from local geometry of the contour, which consist of vertices and edges, connecting adjacent vertices. The image data and desired image features such as image energy are utilized to obtain external forces. The problem of low contrast image data and unclear edges in the image energy is overcome by the proposed algorithm that uses several methods like thresholding, unsupervised clustering methods such as fuzzy C-means (FCM), edge-finding filters like Prewitt, and morphological operations. We also present a method for generating an initial contour for the model from the image data automatically. Evaluation and validation of the methods are conducted by comparing radiologist and automatic segmentation results. The average of the similarity between segmentation results is 0.8 for the left and right thalami indicating excellent performance of the new method. Additional noise and intensity inhomogeneity changed the evaluation results slightly illustrating the robustness of the proposed method to the image noise and intensity inhomogeneity.

Nowadays, various methods have been suggested to measure and monitor blood velocity variation in arteries and veins. Ultrasonic velocimetry is one of these methods, which is based on Doppler shift frequency measurement and the blood flow velocity calculation using Doppler shift signal. Using velocity - time curves or frequency spectrum which is system outputs, the abnormal cases and the stenosis degree can be determined. In this study, the design and prototyping of a pulsed Doppler system are investigated. The design consists of analog and digital circuits. The analog section includes Master oscillator, stimulus generator, transmitter, receiver, RF amplifier, demodulator and signal sampling circuits. Analog Doppler signal is then converted to digital codes and transferred to PC via an analog to digital converter card. The controlling of analog circuits is also implemented by the digital control unit. After data being transferred to the PC, data analysis such as fast fourier transform (FFT), monitoring of blood velocity variation with time and computation of two dimensional spectrogram are implemented by a software which was written in the Visual C++6 environment. In order to test the system, a string Doppler phantom with full electronic control was built. This phantom also can be used to test and control the quality of the other clinical ultrasonic Doppler systems.

The beam pattern profile of an ultrasound array is of great importance in ultrasound imaging. This profile could be enhanced by weighting the elements of array. However, this technique will decrease the signal to noise ratio (S/N) and consequently the quality of the obtained image. In this study, the S/N variation in weighting process is mathematically analyzed, and a new method is proposed to optimize the weighting parameters. The main objective of the method is to provide the desired output of the beam pattern profile of the ultrasound array, as well as the highest possible S/N. The results show that S/N decreases with increasing the main lobe width of beam pattern. The decrease of SIN by weighting in full arrays is higher than in the sparse ones. Also, reducing the focusing depth has the same effect on S/N.

A hierarchical structure model with three levels is presented for modeling motor control in skill movements. At each level, based on accuracy and quality of control, a specific controller is activated. At first level, control concepts are qualitative. The duty of the first level is to provide stability of system, based on the received qualitative information from second level such as the decrement or increment of error. A self-organized controller at first level is used to generate qualitative control commands, and it plays an encouragement-punishment role to keep the stability of system by sending discrete commands to the second level. This controller only contributes at control action when the controller of second level can not preserve stability individually. At second level, control concepts are quantitative. The duty of the second level is adaptation and control of system accurately. The received information at this level generally comes from sensory and visual feedbacks, and it includes more accurate concepts of control action - like the amount of movement error. A model based on the predictive controller at second level generates quantitative control commands and indeed, determines trajectory of movement accurately. A fuzzy switch combines the control commands of first and second levels, based on the sliding mode strategy, to provide a robust control. At third level, this command is interpreted and then is applied to the involved muscles in movement. The received information at this level is generally the contribution of muscles in performing movement and the effects of environment on the movement, which comes from sensory feedbacks. The presented model with this hierarchical structure has a proper ability to control and keep the stability of system. The simulation results confirm this subject.

The generation of electrocardiogram (ECG) signals by using a mathematical model has recently been investigated. One of the applications of a dynamical model which can artificially produces an ECG signal is the easy assessment of diagnostic ECG signal processing devices. In addition, the model may be also used in compression and telemedicine applications. It is also required that the model has capability to produce both normal and abnormal ECG signals. In this study, it is introduced a new method using radial basis function neural networks in a dynamical model based on McSharry model, to produce artificially the ECG signals. This new method has the advantage of capability to simulate a wider class of physiological signals (both normal and abnormal), compared to McSharry model. The simulation results are presented for normal ECG and three abnormal ones. The accuracy of the model has evaluated by using the error functions. The average of this error for a period of 100 seconds using 20 neurons is less than 2.5 percent for the four modeled cases (one normal and three abnormal).

This paper is concerned with developing a force-generating model of electrically stimulated muscle under non-isometric condition. Hill-based muscle models have been the most popular structure. This type of muscle model was constructed as a combination of different independent blocks (i.e., activation dynamics, force-length and force-velocity relations, and series elastic element). The model assumes that the force-length and the force-velocity relations are uncoupled from the activation dynamics.However, some studies suggest that the shapes of the active force-length and the active force-velocity curves change with the level of the activation. Moreover, the "active state" block of the Hill-type model has no physical interpretation. To overcome the limitation of the Hill-type model, we used the multilayer perception (MLP) with back-propagation learning algorithm and Radial Basis Function (RBF) network with stochastic gradient learning rule for muscle modeling, where the stimulation signal, muscle length, velocity of length perturbation, and past measured or predicted force constitute the input of the neural model, and the predicted force is the output. Two modes of network operation are of interest: a time-varying network which allows updating the parameters of network to continue after convergence, and a time-invariant neural network with parameters fixed after convergence. The results show that time-varying and time-invariant neural networks would be able to track the muscle force with accuracy up to 99.5% and 95%, respectively. In addition, the results show that the accuracy of muscle force prediction depends on the structure of neural network. The prediction accuracy of RBF network after 1000 training epochs is higher than that of MLP network after 5000 training epochs.