Study of Physiological Parameters of the Cardiovascular System by One Dimensional and Numerical Simulation
Mehdi
Molaei
M.Sc Student, Faculty Department of Mechanical Engineering, Sharif University
author
Mohammad Saeid
Saeidi
professor,Department of Mechanical Engineering, Sharif University
author
Bahar
Firoozabadi
professor,Department of Mechanical Engineering, Sharif University
author
text
article
2012
per
Study of Physiological Parameters of the Cardiovascular System by One Dimensional and Numerical Simulation. Owning to important role of the cardiovascular system in the human body and increase of cardiovascular diseases from day to day, in this study, we try to simulate a system of arteries by using one dimensional numerical modeling. For the first time in the one dimensional simulation, we use the finite volume method for discretization of Navier-Stocks equations coupled with the state equation. In order to develop the outflow boundary condition, we use a kind of lumped model called arteriole structure tree. Results of this study are verified by results of other one dimensional modeling such as the characteristic method and are showed that finite volume method is able to demonstrate characteristic of blood flow in arteries. Normal pressure and flow profiles in main systemic arteries are determined, and it is founded that the pressure profile becomes steeper with distance from the heart, which is in agreement with physiological patterns. Furthermore, we can show that when elasticity of arteries is increased in arterioscleroses disease, systolic pressure increases, yet diastolic pressure decreases. Finally, according to available results, it is clear that the finite volume method is useful to simulate numerically and one dimensionally the cardiovascular system.
Iranian Journal of Biomedical Engineering
Iranian Society for Biomedical Engineering
5869-2008
5
v.
4
no.
2012
279
288
https://www.ijbme.org/article_13164_fe7afa7416ca5cc8a8753ae167e1fdec.pdf
dx.doi.org/10.22041/ijbme.2012.13164
Voxel Based Treatment Prediction Using Diffusion Anisotropy Indices and Spatial Information in Glioblastoma Multiform Tumor
Hadi
Sabahi
Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran
author
Hamid
Soltanian Zadeh
1Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran
author
Lisa
Scarpace
Hermelin Brain Tumor Research Center, Neurosurgery Department, Henry Ford Health System, Detroit, MI 48202, USA
author
Tom
Mikkelsen
Hermelin Brain Tumor Research Center, Neurosurgery Department, Henry Ford Health System, Detroit, MI 48202, USA
author
text
article
2012
per
In this paper, we propose a method to predict the outcome of Bevacizumab therapy on Glioblastoma Multiform (GBM) tumors. The method uses diffusion anisotropy indices (DAI) and spatial information to predict the treatment response of each tumor voxel. These DAIs are Fractional Anisotropy, Mean Diffusivity, Relative Anisotropy, and Volume Ratio, extracted from Diffusion Tensor Imaging (DTI) data before treatment. The spatial information is considered as the distance of each tumor voxel from the tumor center, extracted from pre-treatment post-contrast T1-weighted Magnetic Resonance Images (pc-T1-MRI). DAIs and spatial information of each tumor voxel are considered as feature vector. DTI and pc-T1-MRI are gathered before and after the treatment of seven GBM patients. First, DAIs of all brain voxels and the distance of each tumor voxel from the tumor center are calculated. Second, the method registers pretreatment DAI maps and post-treatment pc-T1-MRI to pre-treatment pc-T1-MRI. Next, the tumor is segmented using thresholding technique from pc-T1-MRI. Then, Gd-enhanced voxels of the pre- and posttreatment pc-T1-MRI are compared to label the feature vectors. Three classifiers were evaluated, including Support Vector Machine, K-Nearest Neighbor, and Artificial Neural Network. Classification results show a preference for K-Nearest Neighbor based on well-established performance measures.
Iranian Journal of Biomedical Engineering
Iranian Society for Biomedical Engineering
5869-2008
5
v.
4
no.
2012
289
295
https://www.ijbme.org/article_13166_871f0295e2537a4eb845dab560b07644.pdf
dx.doi.org/10.22041/ijbme.2012.13166
Determination of parameters of Fung hyperelastic model for intracranial blood vessel of human using biaxial tensile test
Mohammad
Shafigh
Biomedical Engineering Department, Sciences and Research Branch, Islamic Azad University
author
Nasser
Fatouraee
Biological Fluid Mechanics Research Laboratory, Biomechanics Department, Biomedical Engineering Faculty,
Amirkabir University of Technoligy
author
Amir Saeed
Seddighi
Biomedical Engineering Department, Sciences and Research Branch, Islamic Azad University
author
text
article
2012
per
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.
Iranian Journal of Biomedical Engineering
Iranian Society for Biomedical Engineering
5869-2008
5
v.
4
no.
2012
297
304
https://www.ijbme.org/article_13167_eb2970d99341cc40b2ae56831218dc8a.pdf
dx.doi.org/10.22041/ijbme.2012.13167
Using Heart Rate and Blood Concentration Data in Order to Predict Hypotension of Hemodialysis Patients
Vahid
Abouie
1M.Sc, Faculty of Biomedical Engineering, Amirkabir University of Technology
author
Farzad
Towhidkhah
Associated Professor, Faculty of Biomedical Engineering, Amirkabir University of Technology
author
Vahid Reza
Nafisi
3Assistant Professor, Faculty of Biomedical Engineering, Amirkabir University of Technology
author
Hani
Sharifian
B.Sc, Faculty of Biomedical Engineering, Amirkabir University of Technology
author
text
article
2012
per
Today, Dialysis hypotension during hemodialysis process is the most common problems for about 20 to 30 percent of dialysis patients. In order to avoid this hypotension, blood pressure should be measured during dialysis process continuously and noninvasively But it is practically impossible and few devices for noninvasive and continuous blood pressure measurement are very expensive. Considering this subject, the parameters related to blood pressure should be used to reach this goal. The blood concentrations and heart rate changes are associated with blood pressure in dialysis patients, so in this study, we determined a model by these two parameters in order to predict the blood pressure of hemodialysis patients. After measuring blood concentration, Heart rate and blood pressure from 14 dialysis patients, using neural network model, we determined a new model that can predict blood pressure in dialysis patient by using blood concentration and heart rate data with 3.8 percent error between the real pressure and the pressure that predicted by the model.
Iranian Journal of Biomedical Engineering
Iranian Society for Biomedical Engineering
5869-2008
5
v.
4
no.
2012
305
311
https://www.ijbme.org/article_13168_2015a1f4d60f71556751b913ec233f24.pdf
dx.doi.org/10.22041/ijbme.2012.13168
Novel Methods For Elastography of Soft Tissue Using Ultrasound Waves
Mohammad Hasan
Moradi
Department of Biomedical Engineering, AmirKabir University of Technology
author
Mohammad Sajad
Manuchehri
Department of Biomedical Engineering, AmirKabir University of Technology
author
Reza
IraniRad
Tehran Gastroenterology and Hepatology Center
author
text
article
2012
per
During the centuries, palpation has always been a crucial procedure in diagnosing the diseases. At first, these procedures were invasive, but nowadays numerous attempts by the name of elastographyhave been madeforreaching to noninvasive methods. Elastographys basic datais tissues relative displacement which is tracked by ultrasound waves. First in these systems in order to attain the displacements gradient, an image of tissue is taken and then it is compared to image of that same tissue after applying a small mechanical impulse into it. Mechanical strain is calculated by estimating the displacements gradient and demonstrated as an image with gray levels named elastogram (strains image) .Based on how the mechanical vibration is given, ultrasound-elastography will separate into four categories as follows: static, dynamic, shear-wave and passive elastography. In static-elastography, the force is applied manually by the clinician and therefore it depends on operators skill and cannot be considerable. In dynamic type the movement of tissue is constantly provided by an external vibrator, so in order to prevent the interference of impulses we must use a rapid imaging system that eventually will cost extra expense and unavailability. Shear-wave elastography which currently is the most common method used in elastography systems,has an external vibratorLike dynamic method, but due to momentary impulses, it skips the problem of impulse interference. In passive method, physiologic movements of body will be given to tissue as itsvibration. This technique is hypothetical yet.
Iranian Journal of Biomedical Engineering
Iranian Society for Biomedical Engineering
5869-2008
5
v.
4
no.
2012
313
331
https://www.ijbme.org/article_13169_2202f1423d1f938c9577ce1839ddedf8.pdf
dx.doi.org/10.22041/ijbme.2012.13169
Suitable drug structure prediction with Opposition-Based Differential Evolution
Mohammad
Koohimoghadam
M.Sc Student, Department of Computer Engineering, Iran University of Science and Technology
author
Adel
Torkamaan Rahmani
Assistant Professor, Department of Computer Engineering, Iran University of Science and Technology
author
text
article
2012
per
Discovery of new drugs and study of their side effects has been an important research field in recent years. Because of direct effect of the pharmaceutical products on human health usually the drug design projects are challenging and technically demanding. The incorporation of computer simulations into drug design projects is one of the best ways to optimize drugs' potency. In this approach, researchers try to find the best interaction between protein structure and drug in a virtual environment; this procedure is called "molecular docking". The molecular docking problem can be considered as a search problem. The search space in this problem is defined with all possible protein-ligand interactions and the best interaction is the solution of problem. In this paper, a new approach for finding the best interaction is proposed. The proposed method is based on opposition based differential evolution algorithm. Also the proposed method is enhanced by a local search algorithm and a pseudo-elitism operator. Like other metaheuristic algorithms, our method uses a population of possible solution and AutoDock scoring function is used to evaluate each vector in the population. Six different protein-ligand complexes are used to verify the efficiency of the proposed algorithm. The experimental results show that the proposed algorithm is more robust and reliable than other algorithms such as simulated annealing and Lamarckian genetic algorithm.
Iranian Journal of Biomedical Engineering
Iranian Society for Biomedical Engineering
5869-2008
5
v.
4
no.
2012
333
351
https://www.ijbme.org/article_13170_44d6d2da3e7b83d3199c9da154143f18.pdf
dx.doi.org/10.22041/ijbme.2012.13170
Registration of intra-operative Ultrasound to CT images using Two-stage point based registration algorithm for Correction of Deformation in liver phantom
Fateme
Nazem
M.Sc Student,Physic and Biomedical engineering Department, Tehran University of Medical Sciences
author
Alireza
Ahmadian
Associate Professor, Physic and Biomedical engineering Department, Tehran University of Medical Sciences
author
Mohammad Javad
Abolhasani
Associate Professor,Physic and Biomedical engineering Department, Tehran University of Medical Sciences
author
Nasim
Dadashi
Ph.D Student, Physic and Biomedical engineering Department, Tehran University of Medical Sciences
author
Masoume
Gity
Associate Professor, Radiology Department of Imam khomeini Hospital, Tehran University of Medical Sciences
author
Mohammad Bagher
Shiran
Associate Professor, Physic and Biomedical engineering Department, Tehran University of Medical Sciences
author
text
article
2012
per
Abstract: Image guided liver surgery based on intra-operative ultrasound images has received much attention in recent years. Using an efficient point-based registration method to improve both the accuracy and computational time for registration of pre-deformation CT liver images to post-deformation Ultrasound images is of great concern during surgical procedure. Although, Iterative Closest Point (ICP) algorithm is widely used in surface-based registration, its performance is strongly dependent on existence of noise and initial alignment. The registration technique based on the Unscented Kalman Filter (UKF) proposed recently can be a solution to overcome to noise and outliers on an incremental registration basis but it suffers from computational complexity. To overcome the limitations of ICP and UKF algorithms we proposed an incremental two-stage registration algorithm based on the combination of ICP and UKF algorithm to update the registration process based on arrival of intra-operative images. The two-stage algorithm is examined on phantom data sets. The results of phantom study confirm that the two-stage algorithm outperforms the accuracy of ICP and UKF by 23% and 13%, respectively and reduces the running time of UKF by 60%.
Iranian Journal of Biomedical Engineering
Iranian Society for Biomedical Engineering
5869-2008
5
v.
4
no.
2012
351
358
https://www.ijbme.org/article_24880_43ed0f01736c5a8a8a41b55295e847cb.pdf
dx.doi.org/10.22041/ijbme.2012.24880