فهرست مطالب

Frontiers in Biomedical Technologies - Volume:4 Issue: 1, 2017
  • Volume:4 Issue: 1, 2017
  • تاریخ انتشار: 1396/03/30
  • تعداد عناوین: 5
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  • Maryam Salehnia, Hossein Ghadiri Pages 1-17
    Transcranial focused ultrasound (tFUS) is a safe method with high resolution to stimulate the brain tissue. By appropriate beam-forming, using a phased array transducer enables us to focus on the desired position with high resolution without moving the transducer. In this paper, the physics of tFUS propagated from a linear phased array transducer, in a 2-dimensional environment, is simulated using the k-space pseudospectral method. Furthermore, we study some factors affecting the spatial accuracy of focus point including the length of the transducer and its elements, the beam-forming and the amplitude of input pressure signal. Also, the thickness of the bone layer and the depth of focus as environmental features are considered subsequently. We investigate these parameters to propose optimum conditions in the transcranial focusing. The main contribution of this research includes: 1. computing tissue-sensitive time delays of transducer elements, 2. providing a minimum possible length of the transducer and 3. Using a neural network to determine the best possible value of the amplitude of the input pressure to get desired focus pressure that was not possible before. Based on our experiments, we obtain a significant decrease of about 32 units in the maximum error and fit a function to estimate the pressure with a correlation coefficient of approximately 0.9997.
    Keywords: Transcranial Focused Ultrasound, Phased Array Transducer, Brain Stimulation, Neural Network
  • Pezhman Pasyar, Vahid Sadeghi, Hassan Rezazadeh, Milad Askari, Alireza Mirbagheri, Moayed Alavian, Hossein Arabalibeik Pages 18-30
    Elastography as one of the most promising non-invasive methods in the diagnosis of liver diseases is attracting much attention. An interesting technique which is independent of imaging rate uses shear wave interference patterns induced by two external stimulation sources. In this article a 3D finite element model of liver tissue with superficial mechanical stimulation is presented through which, the possibility of using shear wave interference patterns to determine the type of liver fibrosis is investigated. In addition, the effect of various stimulation characteristics on the propagation of elastic waves and formation of shear wave interference patterns can be measured. Besides, ultrasound imaging and methods based on cross correlation are used to find target displacements caused by interference of shear waves. According to the results, the type of fibrosis was determined at all stages without error, and the mean elasticity estimation error of 4.98% was obtained for the finite element model.
    Keywords: Cross correlation, Elastic waves, Finite element analysis, Interference patterns, Liver elastography, Mechanical stimulation, Ultrasound imaging
  • Sadjad Shafiekhani, Sara Rahbar, Fahimeh Akbarian, Jamshid Hadjati, Armin Allahverdy, Amir Homayoun Jafari Pages 31-37
    Introduction
    Using mathematical models for cancer treatment had excellent outcomes in recent years. Modeling of the tumor-immune interactions is possible by several mathematical models. Stochastic models such as Stochastic Petri Net (SPN) consider the random effects and uncertainty in the biological environments. Therefore, they are a good choice for simulation of biological systems, specially the complex dynamical network of tumor-immune interactions.
    Methods
    In this paper we have modeled the interactions of the B16-F10 tumor cells, Cytotoxic T cells (CTL) and Myeloid Derived Suppressor cell (MDSC) by SPN. By systematic search on immunology resources, we identified the behaviors, characteristics, and effective interactions between these cells. We used SPN to construct the dynamics of these cells, therefore a dynamical network of tumor-immune interactions (DNTII) has been made. By considering these cells as places and all interactions as transitions of SPN, we can simulate this complex biological network. The model has control parameters that their regulation causes DNTII to mimic different behaviors of tumor-immune system.
    Results
    The model can properly simulate dynamical complex network of tumor-immune interactions compared to biological reality. This model is capable to represent different behavior of tumor-immune system such as tumor escape from immune response, overcoming the immune system on the tumor cells and equilibrium of the tumor and immune system.
    Conclusion
    By using this model, we can test different immunology hypothesis in a simulation environment without spending any time and cost.
    Keywords: stochastic mode, B16-F10 tumor cell, MDSC, stochastic petri net
  • Alireza Mardanshahi, Seyed Mohammad Abedi, Mohammad Shokrzadeh Pages 38-41
    Breast cancer is the second leading cause of cancer death among women. One in eight women will be diagnosed with breast cancer in their life period .There are several risk factors for progress of carcinogen cell. Zinc and lead have crucial role in oxidative stress and changes in their level can enhance progress of carcinogen cells. The purpose of this study is to discover the level of zinc and lead in plasma of breast cancer patients. One hundred numbers with breast carcinoma diagnosis was confirmed by pathological samples and the results were compared with standard values and amount of these elements was observed in the blood by atomic absorption. Zinc plasma level in patients was found to be 50.59.72 (μg/dl) with p value of p=0.003 which is lower than standard value (80 μg/dl). On the other hand, the plasma lead level was identified to be 6.24.173(μg/dl) in patients and zero in the standard value. According to the observation, breast cancer patients had a higher lead level than standard value and lower zinc level. Zinc acts as an important free-radical scavenger to protect cells and deficiency of that can increases progress of carcinogen cells. Therefore, use of trace elements and also the chelators of heavy metals are necessary to prevent breast cancer in high risk individuals.
    Keywords: Zinc, Lead, Breast cancer
  • Sadegh Shurche, Nader Riahi Alam Pages 42-48
    Purpose
    Geometric errors in images called image distortion are one of the main problems in magnetic resonance imaging, including 3D imaging, measuring blood flow velocity, functional imaging and treatment planning in radiation therapy. The geometric distortion in MRI images is due to the non-uniformity of the magnetic field and nonlinearity of gradients. In this study, the accuracy and the repeatability of the images were evaluated respectively by phantom measuring and repeating the measurements in the phantom and then we correct these geometric errors.
    Methods
    The magnetic resonance imaging of the phantom was performed on the 3 Tesla Siemens Prisma Model to measure the geometric distortion using the network pattern. The spin echo protocol was repeated three times with T1, T2 and PD weightings to measure the repeatability of image distortion. Image distortion was evaluated by measuring the distance between the edges using a MATLAB program. Furthermore, non-uniformity of the magnetic field and the nonlinearity of the gradients were examined using appropriate phantoms.
    Result
    The average error obtained in the 25 cm field of view was 1 pixel in both directions x and y (each pixel was 1.024 mm). Given the images by phantom, the device gradient was linear. Furthermore, considering the B1 and B0 fields’ measurements, the B0 of the device was 0.3125 ppm over a 24 cm DSV (diameter of spherical volume).
    Conclusion
    Since the brain coil displacement was 1 pixel, the device could be used in 3DMRI, velocity MRI, FMRI and RTTP.
    Keywords: Magnetic Resonance Imaging, Artifacts, Phantom, Reproducibility