Journal of Biomedical Physics & Engineering
Volume:3 Issue: 1, Jan-Feb 2013

The artificial neural networks (ANNs) are useful in solving nonlinear processes, without the need for mathematical models of the parameters. Since the relationship between the CT numbers and material compositions is not linear, ANN can be used for obtaining tissue density and composition. The aim of this study is to utilize ANN for determination of the composition and mass density of different tissues to be used in Monte Carlo simulation in treatment planning of brachytherapy. The ANN were used for mass density calibration. The density and composition of several human body tissues, along with their corresponding CT numbers are used as the training samples. Finally, when the ANN is trained, the neural network would give us the material information, i.e. mass density, electron density, and material composition, by entering the CT numbers of different tissues into the network as its input. The tissue compositions and densities predicted by the ANN for each CT number were compared with the real values of such parameters. The tissue parameters predicted by the ANN were used as the phantom materials for obtaining the dose at different distances from Pd-103 and Cs-137 brachytherapy sources. Finally, the doses at different distances of the real phantoms were compared with doses inside the phantoms predicted by Neural Network. According to the results of these studies, the Neural Network algorithm used in this investigation can be used for accurate prediction of the material compositions of different tissues. For example, it can give the mass densities of bone, muscle, and water with the percentage differences of 0.52%, -0.95%, and 0% respectively. Comparison of the dose distribution inside the water phantom predicted by ANN and the real water phantom shows a percentage difference of less than 0.66% and 2% for Cs-137 and Pd-103, respectively. The results of this study indicate that the Artificial Neural Networks are applicable in determination of tissue density and material compositions from the CT images data, and the material compositions and density of the phantoms (bone, muscle, and water) obtained by this method can be used for material definition in Monte Carlo simulations.

MR imaging is one of the best diagnostic modalities in medicine. During an MR imaging examination, three types of field are employed to produce images. Various experimental studies have been performed about the effects of each single type of field but only few studies are available on their combination to generate MR imaging. The main objective of this research work is to study the effects of MR imaging on the level of glucose and cortisol hormone. 40 adult male Wistar rats (220 ± 10 g) were randomly divided into 2 groups of exposed (20) and control (20) groups. They were exposed under diagnostic MR imaging of 1.5 T field strengths for 25 minutes. Then, immediately blood samples were isolated and level of serum glucose measured by Auto-analyzer and cortisol content of blood sera was assayed by radioimmunoassay (RIA). Findings showed significant decreases in the levels of glucose and cortisol hormone after 25 minutes of exposure. MR imaging may have adverse effects on the level of glucose and cortisol hormone.

Irreversible electroporation (IRE) is a novel tumor ablation technique. IRE is associated with high electrical fields and is often reported in conjunction with thermal damage caused by Joule heating. For good response to surgery it is crucial to produce minimum thermal damage in both tumoral and healthy tissues named Non-Thermal Irreversible Electroporation(NTIRE). Non-thermal irreversible electroporation attempts have concentrated on tumor ablation with strong electric field with producing minimum thermal damage. To establish a Multi Objective Genetic Algorithm (MOGA) for IRE treatment planning. Numerical modeling and genetic programming were coupled to optimize thermal and electrical distribution in tissue. A 3D MRI based model was established and treatment parameters such as electrode thickness, electrode insertion, distance between electrode and applied voltage were optimized. Prefect tumor ablation with IRE surgery with relatively little electrical and thermal damage on healthy tissue can be achieved by using genetic algorithm optimization. Such optimization can trade off between perfect tumor coverage and damage to healthy tissue. Concerning the thermal aspect of IRE surgery. The established multi-objective genetic algorithm based treatment planning system, can optimize both geometric and electric parameters in IRE surgery. Such optimization result in prefect tumor ablation as well as minimum thermal damage to both normal and tumoral tissue.

Ramsar, a northern city in Iran, lies on the coast of the Caspian Sea. This city has areas with some of the highest levels of natural radiation measured to date. The radon concentration in some areas of Ramsar is up to 3700 Bq m–3. On the other hand, due to high level of humidity, damp-proof barriers should be used in construction of new buildings in radon prone areas of this city. Montmorillonite clays can be used as both moisture-proof and radon-proof agents. This study was an attempt to investigate the radon-proof properties of montmorillonite nanoclay in construction of new buildings in radon prone areas.Methods and Although soaked nanoclay samples could not reduce the radon level, when wet nano-montmorillonite was used, mean radon level inside the house was 1082.4 ± 5.9 Bq/m3 (ranged 826.3 – 11.40.5 Bq/m3) while removing nano-montmorillonite sheet increased the radon level to 1146.5 ± 6.2 Bq/m3. The high moisture in the soil of these areas, makes the nano-montmorillonite wet and converts it to a good radon-proof sheet. It is worth mentioning that the nano-montmorillonite clay used in this study is not supposed to replace radon-barrier (membrane) sheets but when used with proper membranes can enhance the efficiency of radon mitigation systems.