Spinal Biomechanics
Mojtaba Shahab; Behzad Seyfi; Nasser Fatouraee; Amir Saeid Seddighi
Volume 9, Issue 1 , April 2015, , Pages 1-15
Abstract
Spinal deformities are generally associated with lumbar and cervical chronic pain and additionally they disturb the health. In these deformities, lumbar spinal curvature undergone changes in three dimensional space and in most cases, they cause reduction of lung capacities, breathing problems and negative ...
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Spinal deformities are generally associated with lumbar and cervical chronic pain and additionally they disturb the health. In these deformities, lumbar spinal curvature undergone changes in three dimensional space and in most cases, they cause reduction of lung capacities, breathing problems and negative effects on cardiovascular system. In critical deformity cases, in order to correct the deformity and prevent its progression, surgeons determine to perform posterior spinal fusion. As a result, they need to extract some important clinical parameters of spine such as Cobb angle, sagittal and coronal balance, spinal curvature, vertebraes angles and their rotations. In this study, edited tomographic images in MIMICS, were used to prepare a three dimensional model of the spine. Then by using curve fitting techniques and different clustering methods such as self-organization nueral network, k-means and hierarchical method, vertebras were separated and important geometrical data such as curvature of the spine and vertebras angle were obtained. In addition, through implementation of certain algorithms, other clinical features of each vertebra, including minimum and maximum height, length and width of the vertebral body and the relative displacement of vertebras were calculated automatically. In order to validate the proposed methods, measures and angles; derived values obtained automatically at each stage, were again calculated by a radiologist and a spine surgeon who was unaware of the goals of the research. Automatic values were verified by being compared with these manual results. In conclusion the reliability, accuracy and performance of the proposed automatic algorithms were demonstrated.
Cardiovascular Biomechanics
Mohammad Shafigh; Nasser Fatouraee; Amir Saeed Seddighi
Volume 5, Issue 4 , June 2011, , Pages 297-304
Abstract
Understanding of mechanical properties of healthy brain arteries is a key element in the development of clinical diagnosis and prevention.For this reason we make biaxial measurements to have appropriate parameters for the underlying material models. To acquire these properties, eight samples were obtained ...
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Understanding of mechanical properties of healthy brain arteries is a key element in the development of clinical diagnosis and prevention.For this reason we make biaxial measurements to have appropriate parameters for the underlying material models. To acquire these properties, eight samples were obtained from middle cerebral arteries of human cadavers, whose death were not due to injuries or diseases of cerebral vessels, and tested within twelve hours after resection. The changes of force and deformation until the vessel rupture were recorded. Thereafter, the stress-strain curves were plotted and fitted with a hyperelastic five-parameter Fung model parameters, according to the best fit, were determined. It was found that the arteries were remarkably stiffer in circumferential than in axial direction. It was also found that the use of multi-parameter hyperelastic constitutive models is applicable for mathematical description of behavior of cerebral vessel tissue. The reported material properties can be a proper reference for numerical simulation of cerebral arteries of healthy or diseased intracranial arteries.
Tissue Engineering
Farhad Farmanzad; Siamak Najarian; Mohammad Reza Eslami; Amir Saeed Seddighi
Volume 1, Issue 4 , June 2007, , Pages 281-288
Abstract
Two different types of computer modeling, i.e., the elastic and hyperelastic plane strain models were employed and compared with each other. Using finite element analysis, we determined a suitable model for describing the biomechanical behavior of the brain, especially the deformation and displacement ...
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Two different types of computer modeling, i.e., the elastic and hyperelastic plane strain models were employed and compared with each other. Using finite element analysis, we determined a suitable model for describing the biomechanical behavior of the brain, especially the deformation and displacement of the brain ventricles. The CT-Scan of an epidural hematoma patient was modeled using both approaches. Then, by varying the mechanical parameters of the tissue (i.e., C10, C01, E, and v) and the internal ventricular pressure, the displacement rate of the corresponding points in the ventricles was simulated. Finally, the results of the simulation were compared with those of the actual ventricles, and then, the data set with the least amount of error was identified. For various types of loadings and with different pressure gradients, the results of the simulation show that if the effect of an increase in the internal pressure of the ventricles is neglected, it will lead to unrealistic results. Particularly, in unidirectional strain loading with a pressure gradient of zero (AP= 0), the walls of the ventricle adjacent to the hematoma will collapse completely. The best results were obtained for the elastic model where ΔP = 9.4 mmHg (1.25 kPa) and for the hyperelastic model where ΔP = 7.5 mmHg (1.00 kPa). These findings are consistent with the clinical conditions of the patient. In the plane strain biomechanical modeling, for unidirectional strain loading (conditions which are similar to the application of navigation systems in surgeries), neglecting the geometry and the variation of the internal pressure of the ventricles will not lead to acceptable results. Taking into account the abovementioned parameters in describing the mechanical behavior of the brain (for epidural hematoma lesions), the elastic model (88.7% average relative accuracy) brings about better results compared with those of the hyperclastic model (86.9% average relative accuracy).
