A numerical-experimental study on thermal evaluation of orthodontic tooth movement during initial phase of treatment.

The most desired target of orthodontic treatment is tooth movement as a result of application of efficient force system. In this study, effect of tooth loading is studied on temperature profile around the tooth at early stages of treatment. The basis of temperature variation is increase of cell number and activities in periodontium as a result of compression and tension of this layer. Highest cellular activities occur in the beginning of loading procedure and aim to reduce mechanical stress in the periodontium which finally ends up with orthodontic tooth movement during couple of years. To find out the correlation between temperature variation and the applied force, in vivo experiments are conducted on ten rats and temperature is measured in specific time periods. It is observed that temperature is higher in direction of the net force about 0.3℃. Next, numerical finite element analysis is carried out on the rat tooth model. Mechanical stress results show that regions with compressive stress have rather high temperature in the experiments. Mechanical stress on periodontium-bone interface is multiplied by a coefficient to simulate cellular activities on this boundary as a heat source and thermal analysis is carried out to obtain temperature profile. The thermo-mechanical coefficient is identified for each rat by imposing the experimental temperatures on numerical outputs. For assessment of a treatment efficiency and deduction of the applied force, temperatures could be measured experimentally and compared with the corresponding numerical analysis temperature result obtained by employing the thermo-mechanical coefficient found earlier for each rat.

Characterization of breast tissue permeability for detection of vascular breast tumors: An in vitro study.

The biological tissue could be considered as a porous matrix filled with the interstitial fluid. The tumoral tissue has a different permeability from the healthy tissue. With regard to this knowledge, the main objective of the present study is to study the tissue permeability as a diagnostic parameter for the detection of breast cancer. To this end, the healthy and the cancerous specimens of the breast tissue are taken from 17 female cases. All cancerous tumors are in the vascular phase of the growth. The samples undergo a uniaxial compression test by a robotic system while the strain rate is set to remain unchanged. Using the stress and the strain rate data, the strain-dependent permeability is determined, which is an exponential function of the strain level. The permeability function is identified by the initial permeability at the zero compressive strain and a material constant. Results show that the initial permeability of the healthy breast tissue is significantly different from the corresponding value for the cancerous tissue. For all cancerous samples, the permeability is less than the healthy tissue samples; as 40-70% reduction in the initial permeability is observed compared to the healthy breast tissue. The evaluation of the permeability between the healthy and the cancerous specimens is accompanied by the biopsy reports and observing structural images of the specimens using environmental scanning electron microscope. Based on the results, the permeability is suggested to have a diagnostic application for the detection of vascular breast tumors.

Early relaxation time assessment for characterization of breast tissue and diagnosis of breast tumors.

During recent years, many efforts have been made to increase the efficiency of the treatment methods by adjusting the treatment with the specification of related tissue. Non-penetrating indentation test is a non-invasive method for characterizing mechanical behavior of the tissue. In this study, characterization of the viscoelastic behavior of breast tissue is investigated by means of the relaxation time. To this end, an in-vivo study is conducted on the breast tissue of 18 female cases by performing a compressive indentation test with controlled rate of deformation. The test is performed in two subsequent stages; at first stage, the tissue is compressed by a specified strain rate and is called the loading procedure. At the second stage, which is the unloading procedure, the load is removed by the same rate and the tissue behavior is studied while it returns to the unloaded position. A generalized Maxwell model with two Maxwell-arms is used to model the tissue's viscoelastic response. Force vs. compression data during the unloading course, are obtained and interpolated by the transfer function of the viscoelastic model and two relaxation times are obtained. The smaller in value is referred to as the early relaxation time and results show that it has variation with the tissue's mechanical structure, which makes it suitable as a characteristic property. This property has insignificant variation over each individual's breast tissue, meanwhile major variation is observed between different cases and also in the presence of a tumor. Results show that existence of tumor in the breast tissue can increase the early relaxation time up to 6 times more than the corresponding value at the healthy tissue. Based on case-dependent character of the early relaxation time, tumor detection procedure based on the elevation of characteristic early relaxation time should be performed individually for each case.

A novel approach for early evaluation of orthodontic process by a numerical thermomechanical analysis.

The main objective of this paper is to propose a novel method that provides an opportunity to evaluate an orthodontic process at early phase of the treatment. This was accomplished by finding out a correlation between the applied orthodontic force and thermal variations in the tooth structure. To this end, geometry of the human tooth surrounded by the connective soft tissue called the periodontal ligament and the bone was constructed by employing dental CT scan images of a specific case. The periodontal ligament was modeled by finite strain viscoelastic model through a nonlinear stress-strain relation (hyperelasticity) and nonlinear stress-time relation (viscoelasticity). The tooth structure was loaded by a lateral force with 15 different quantities applied to 20 different locations, along the midedge of the tooth crown. The resultant compressive stress in the periodontal ligament was considered as the cause of elevated cell activity that was modeled by a transient heat flux in the thermal analysis. The heat flux value was estimated by conducting an experiment on a pair of rats. The numerical results showed that by applying an orthodontic force to the tooth structure, a significant temperature rise was observed. By measuring the temperature rise, the orthodontic process can be evaluated.

Quantitative diagnosis of breast tumors by characterization of viscoelastic behavior of healthy breast tissue.

The main objective of the present study is to propose a model for characterizing the viscoelastic behavior of the healthy breast tissue. The study population consisted of 60 healthy regions of the breast tissue belonged to 9 female cases. To accomplish this, the attending cases were examined by a robot-assisted device and the mechanical stress resulted from an applied compressive strain was measured. Correlation between the experimental stress and the strain data identifies the breast tissue mechanical behavior. The tissue behavior was modeled by a five-element Maxwell-Wiechert model called model E. The model was personalized for every attending case via its coefficients based on a personalized diagnosis idea. The model performance was assessed by measuring the Mean Squared Error (MSE) and the match percentage of the model to the experimental data. Moreover, the model performance was compared with three common spring-dashpot models included the Maxwell model, the Burgers model and Standard Linear Solid model. Results affirmed that model E had the best data match in the whole mechanical loading and the MSE was considerably reduced. Subsequently model E was implemented for the tumor-included regions among the population study. Results showed that with a high match percentage, coefficients had significant deviations from the corresponding healthy regions' values for every individual. Consequently, personalized model E can be used for the healthy tissue characterization and tumor detection.

Dynamic modeling of breast tissue with application of model reference adaptive system identification technique based on clinical robot-assisted palpation.

Accurate identification of breast tissue's dynamic behavior in physical examination is critical to successful diagnosis and treatment. In this study a model reference adaptive system identification (MRAS) algorithm is utilized to estimate the dynamic behavior of breast tissue from mechanical stress-strain datasets. A robot-assisted device (Robo-Tac-BMI) is going to mimic physical palpation on a 45 year old woman having a benign mass in the left breast. Stress-strain datasets will be collected over 14 regions of both breasts in a specific period of time. Then, a 2nd order linear model is adapted to the experimental datasets. It was confirmed that a unique dynamic model with maximum error about 0.89% is descriptive of the breast tissue behavior meanwhile mass detection may be achieved by 56.1% difference from the normal tissue.

Geometrical features assessment of liver's tumor with application of artificial neural network evolved by imperialist competitive algorithm.

Geometrical features of a cancerous tumor embedded in biological soft tissue, including tumor size and depth, are a necessity in the follow-up procedure and making suitable therapeutic decisions. In this paper, a new socio-politically motivated global search strategy which is called imperialist competitive algorithm (ICA) is implemented to train a feed forward neural network (FFNN) to estimate the tumor's geometrical characteristics (FFNNICA). First, a viscoelastic model of liver tissue is constructed by using a series of in vitro uniaxial and relaxation test data. Then, 163 samples of the tissue including a tumor with different depths and diameters are generated by making use of PYTHON programming to link the ABAQUS and MATLAB together. Next, the samples are divided into 123 samples as training dataset and 40 samples as testing dataset. Training inputs of the network are mechanical parameters extracted from palpation of the tissue through a developing noninvasive technology called artificial tactile sensing (ATS). Last, to evaluate the FFNNICA performance, outputs of the network including tumor's depth and diameter are compared with desired values for both training and testing datasets. Deviations of the outputs from desired values are calculated by a regression analysis. Statistical analysis is also performed by measuring Root Mean Square Error (RMSE) and Efficiency (E). RMSE in diameter and depth estimations are 0.50 mm and 1.49, respectively, for the testing dataset. Results affirm that the proposed optimization algorithm for training neural network can be useful to characterize soft tissue tumors accurately by employing an artificial palpation approach.

A novel tactile-guided detection and three-dimensional localization of clinically significant breast masses.

This paper presents a novel robotic sensory system 'Robo-Tac-BMI', which manipulates an indentation probe for the detection and three-dimensional localization of an abnormal mass embedded in the breast tissue. The Robo-Tac-BMI is designed based on artificial tactile sensing technology which is a new non-invasive method for mimicking the surgeon's palpation quantitatively. The intelligent processor of the device provides an overall stiffness map of the scanned areas. The extracted stiffness parameters provide a decisive factor for certifying the mass existence. Results are validated by 'gold standard' tests. Following the mass detection, its 3D localization is of essential importance in the treatment procedures. The planar 2D coordinate is readily available for all points on the tissue surface. Mass depth estimation is achieved by a comprehensive model utilizing the logistic regression algorithm and a Receiver Operating Characteristic (ROC) Curve for the highest accuracy. Statistical analysis is performed over 27 cases with 346 scanned areas.

Coupled fluid-wall modelling of steady flow in stenotic carotid arteries.

Arterial stenoses may cause critical blood flow and wall conditions leading to clinical complications. In this paper computational models of stenotic carotid arteries are proposed and the vessel wall collapse phenomenon is studied. The models are based on fluid-structure interactions (FSI) between blood and the arterial walls. Coupled finite element and computational fluid dynamics methods are used to simultaneously solve for stress and displacement in the solid, and for pressure, velocity and shear stress in the fluid domain. Results show high wall shear stress at the stenosis throat and low (negative) values accompanied by disturbed flow patterns downstream of the stenosis. The wall circumferential stress varies abruptly from tensile to compressive along the stenosis with high stress concentration on the plaque shoulders showing regions of possible plaque rupture. Wall compression and collapse are observed for severe cases. Post-stenotic collapse of the arterial wall occurs for stenotic severity as low as 50%, with the assumption that a given amount of blood flow needs to pass the stenotic artery; whereas if constant pressure drop should be maintained across a constriction, then collapse happens at severity of 75% and above. The former assumption is based on the requirement of adequate blood supply to the downstream organs/tissue, while the latter stems from the fact that the pumping mechanism of the body has a limited capacity in regulating blood pressure, in case a stenosis appears in the vasculature.