4 resultados para Tissue Destruction

em Repositório Científico do Instituto Politécnico de Lisboa - Portugal


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Liver steatosis is mainly a textural abnormality of the hepatic parenchyma due to fat accumulation on the hepatic vesicles. Today, the assessment is subjectively performed by visual inspection. Here a classifier based on features extracted from ultrasound (US) images is described for the automatic diagnostic of this phatology. The proposed algorithm estimates the original ultrasound radio-frequency (RF) envelope signal from which the noiseless anatomic information and the textural information encoded in the speckle noise is extracted. The features characterizing the textural information are the coefficients of the first order autoregressive model that describes the speckle field. A binary Bayesian classifier was implemented and the Bayes factor was calculated. The classification has revealed an overall accuracy of 100%. The Bayes factor could be helpful in the graphical display of the quantitative results for diagnosis purposes.

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Aim - To use Monte Carlo (MC) together with voxel phantoms to analyze the tissue heterogeneity effect in the dose distributions and equivalent uniform dose (EUD) for (125)I prostate implants. Background - Dose distribution calculations in low dose-rate brachytherapy are based on the dose deposition around a single source in a water phantom. This formalism does not take into account tissue heterogeneities, interseed attenuation, or finite patient dimensions effects. Tissue composition is especially important due to the photoelectric effect. Materials and Methods - The computed tomographies (CT) of two patients with prostate cancer were used to create voxel phantoms for the MC simulations. An elemental composition and density were assigned to each structure. Densities of the prostate, vesicles, rectum and bladder were determined through the CT electronic densities of 100 patients. The same simulations were performed considering the same phantom as pure water. Results were compared via dose-volume histograms and EUD for the prostate and rectum. Results - The mean absorbed doses presented deviations of 3.3-4.0% for the prostate and of 2.3-4.9% for the rectum, when comparing calculations in water with calculations in the heterogeneous phantom. In the calculations in water, the prostate D 90 was overestimated by 2.8-3.9% and the rectum D 0.1cc resulted in dose differences of 6-8%. The EUD resulted in an overestimation of 3.5-3.7% for the prostate and of 7.7-8.3% for the rectum. Conclusions - The deposited dose was consistently overestimated for the simulation in water. In order to increase the accuracy in the determination of dose distributions, especially around the rectum, the introduction of the model-based algorithms is recommended.

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The MCNPX code was used to calculate the TG-43U1 recommended parameters in water and prostate tissue in order to quantify the dosimetric impact in 30 patients treated with (125)I prostate implants when replacing the TG-43U1 formalism parameters calculated in water by a prostate-like medium in the planning system (PS) and to evaluate the uncertainties associated with Monte Carlo (MC) calculations. The prostate density was obtained from the CT of 100 patients with prostate cancer. The deviations between our results for water and the TG-43U1 consensus dataset values were -2.6% for prostate V100, -13.0% for V150, and -5.8% for D90; -2.0% for rectum V100, and -5.1% for D0.1; -5.0% for urethra D10, and -5.1% for D30. The same differences between our water and prostate results were all under 0.3%. Uncertainties estimations were up to 2.9% for the gL(r) function, 13.4% for the F(r,θ) function and 7.0% for Λ, mainly due to seed geometry uncertainties. Uncertainties in extracting the TG-43U1 parameters in the MC simulations as well as in the literature comparison are of the same order of magnitude as the differences between dose distributions computed for water and prostate-like medium. The selection of the parameters for the PS should be done carefully, as it may considerably affect the dose distributions. The seeds internal geometry uncertainties are a major limiting factor in the MC parameters deduction.

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The cleaning of syngas is one of the most important challenges in the development of technologies based on gasification of biomass. Tar is an undesired byproduct because, once condensed, it can cause fouling and plugging and damage the downstream equipment. Thermochemical methods for tar destruction, which include catalytic cracking and thermal cracking, are intrinsically attractive because they are energetically efficient and no movable parts are required nor byproducts are produced. The main difficulty with these methods is the tendency for tar to polymerize at high temperatures. An alternative to tar removal is the complete combustion of the syngas in a porous burner directly as it leaves the particle capture system. In this context, the main aim of this study is to evaluate the destruction of the tar present in the syngas from biomass gasification by combustion in porous media. A gas mixture was used to emulate the syngas, which included toluene as a tar surrogate. Initially, CHEMKIN was used to assess the potential of the proposed solution. The calculations revealed the complete destruction of the tar surrogate for a wide range of operating conditions and indicated that the most important reactions in the toluene conversion are C6H5CH3 + OH <-> C6H5CH2 + H2O, C6H5CH3 + OH <-> C6H4CH3 + H2O, and C6H5CH3 + O <-> OC6H4CH3 + H and that the formation of toluene can occur through C6H5CH2 + H <-> C6H5CH3. Subsequently, experimental tests were performed in a porous burner fired with pure methane and syngas for two equivalence ratios and three flow velocities. In these tests, the toluene concentration in the syngas varied from 50 to 200 g/Nm(3). In line with the CHEMKIN calculations, the results revealed that toluene was almost completely destroyed for all tested conditions and that the process did not affect the performance of the porous burner regarding the emissions of CO, hydrocarbons, and NOx.