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Monte Carlo (MC) methods for radiotherapy dose calculation are widely accepted as capable of achieving high accuracy. In particular, MC calculations have been demonstrated to successfully reproduce measured dose distributions in complex situations where alternative dose calculation algorithms failed (for example, regions of charged particle disequilibrium). For this reason, MC methods are likely to play a central role in radiotherapy dose calculations and dose verification in the future. However, clinical implementations of MC calculations have typically been limited due to the high computational demands. In order to improve the feasibility of using MC simulations clinically, the simulation techniques must be made more efficient. This dissertation presents a number of approaches to improve the efficiency of MC dose calculations. One of the most time consuming parts of source modeling is the simulation of the secondary collimators, which absorb particles to define the rectangular boundaries of radiation fields. The approximation of assuming negligible transmission through and scatter from the secondary collimators was evaluated for accuracy and efficiency using both graphics processing unit (GPU)-based and central processing unit (CPU)-based MC approaches. The new dose calculation engine, gDPM, that utilizes GPUs to perform MC simulations was developed to a state where accuracy comparable to conventional MC algorithms was attained. However, in GPU- based dose calculation, source modeling was found to be an efficiency bottleneck. To address this, a sorted phase-space source model was implemented (the phase-space- let, or PSL model), as well as a hybrid source model where a phase-space source was used only for extra-focal radiation and a point source modeled focal source photons. All of these methods produced results comparable with standard CPU-based MC simulations in minutes, rather than hours, of calculation time. While maintaining reasonable accuracy, the hybrid source model increased source generation time by a factor of ~2-5 when compared with the PSL source model. A variance reduction technique known as photon splitting was also implemented into gDPM, to evaluate its effectiveness at reducing simulation times in GPU calculations. Finally, an alternative CPU-based MC dose calculation technique was presented for specific applications in pre-treatment dose verification. The method avoids the requirement of plan-specific MC simulations. Using measurements from an electronic portal imaging device (EPID), pre-calculated MC beamlets in a spherical water phantom were modulated to obtain a dose reconstruction. / Graduate

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This thesis describes the development of a novel comprehensive Monte Carlo simulation code, ARTEMIS, for the investigation of electron-hole pair transport mechanisms in a-Se x-ray imaging detectors. ARTEMIS allows for modeling of spatiotemporal carrier transport in a-Se, combining an existing Monte Carlo simulation package, PENELOPE, for simulation of x-ray and secondary electron interactions and new routines for electron-hole pair transport with three-dimensional spatiotemporal signal output considering the effects of applied electric field. The detector Swank factor, an important imaging performance metric is calculated from simulated pulse-height spectra and shown to depend on incident x-ray energy and applied electric field. Simulation results are compared to experimental measurements and are found to agree within 2%. Clinical x-ray spectra are also used to study detector performance in terms of energy weighting and electronic noise. Simulation results show energy-weighting effects are taken into account in the ARTEMIS model, where the Swank factor and DQE have a higher dependence on the high-energy incident x rays due to increased carrier yield. Electronic noise is found to widen the pulse-height spectra and degrade the Swank factor. The effect of recombination algorithms and burst models are studied. A comparison of a first-hit algorithm and a nearest-neighbor approach shows no significant difference in the simulation output while achieving reduced simulation time. The examination of the initial generation of carriers in the burst shows that the recombination efficiency of carriers is dependent on the carrier density and electric field. Finally, the spatial resolution characteristics of a flat-panel a-Se detector are studied by using the ARTEMIS model for spatial output and image generation. The modulation transfer functions are calculated from simulated detector point response functions for monoenergetic and clinical radiation qualities.

The effects of the microstructure of metal films on electric component performance and longevity have become increasingly important with the recent advances in nanotechnology. Depending on the application of the metal films and interconnects, certain microscopic structures and properties are preferred over others. A common method to produce these films and interconnects is through electrodeposition. As with every process, the ability to control the end product requires a detailed understanding of the system and the effect of operating conditions on the resulting product. To address this problem, a three-dimensional on-lattice kinetic Monte Carlo (KMC) method is developed to conduct atomistic simulations of single crystal and polycrystalline metal electrodeposition. The method utilizes the semi-empirical multi-body embedded-atom method (EAM) potential that accounts for the cohesive forces in a metallic system. The resulting computational method, KMC-EAM, enables highly descriptive simulations of electrodeposition processes to be performed over experimentally relevant scales. In this work, kinetically controlled copper electrodeposition onto single crystal copper under galvanostatic direct-current conditions and polycrystalline copper under potentiostatic direct-current conditions is modelled using the aforementioned KMC method. Four types of surface processes are considered during electrodeposition: deposition, dissolution, surface diffusion and grain boundary diffusion. The equilibrium microstructures from single crystal experiments were validated using molecular dynamics (MD) simulations through the comparison of energy per atom and average coordination number. The growth mode observed is in agreement with experimental results for the same orientation of copper. MD simulation relaxes constraints and approximations resulting from the use of KMC. Results indicate that collective diffusion mechanisms are essential in order to accurately model the evolution of coating morphology during electrodeposition. In the polycrystalline simulations, the effect of surface energy is taken into account in the propensities of deposition and dissolution. Sub-surface grain volume measurements were obtained from simulation results and the grain volume evolution with time is in agreement with both qualitative observations based on the deposit morphology and surface energy calculations. Simulations of polycrystalline deposition agree with findings from experimental studies that the evolution of the root-mean-squared roughness of the deposit during the early stages of deposition follows a power law relationship with respect to time $\approx t^{n}$. Furthermore, the power law exponent on time is determined to be $n \approx 0.5$, also in agreement with the experimental values reported in the literature.

In the field of radiation protection, complex computationally expensive algorithms are used to predict radiation doses, to organs in the human body from exposure to internally deposited radionuclides. These algorithms contain many inputs, the true values of which are uncertain. Current methods for assessing the effects of the input uncertainties on the output of the algorithms are based on Monte Carlo analyses, i.e. sampling from subjective prior distributions that represent the uncertainty on each input, evaluating the output of the model and calculating sample statistics. For complex computationally expensive algorithms, it is often not possible to get a large enough sample for a meaningful uncertainty analysis. This thesis presents an alternative general theory for uncertainty analysis, based on the use of stochastic process models, in a Bayesian context. The measures provided by the Monte Carlo analysis are obtained, plus extra more informative measures, but using a far smaller sample. The theory is initially developed in a general form and then specifically for algorithms with inputs whose uncertainty can be characterised by independent normal distributions. The Monte Carlo and Bayesian methodologies are then compared using two practical examples. The first example, is based on a simple model developed to calculate doses due to radioactive iodine. This model has two normally distributed uncertain parameters and due to its simplicity an independent measurement of the true uncertainty on the output is available for comparison. This exercise appears to show that the Bayesian methodology is superior in this simple case. The purpose of the second example is to determine if the methodology is practical in a 'real-life' situation and to compare it with a Monte Carlo analysis. A model for calculating doses due to plutonium contamination is used. This model is computationally expensive and has fourteen uncertain inputs. The Bayesian analysis compared favourably to the Monte Carlo, indicating that it has the potential to provide more accurate uncertainty analyses for the parameters of computationally expensive algorithms.

Monte Carlo method has been successfully applied in simulating the particles transport problems. Most of the Monte Carlo simulation tools are static and they can only be used to perform the static simulations for the problems with fixed physics and geometry settings. Proton therapy is a dynamic treatment technique in the clinical application. In this research, we developed a method to perform the dynamic Monte Carlo simulation of proton therapy using Geant4 simulation toolkit. A passive-scattering treatment nozzle equipped with a rotating range modulation wheel was modeled in this research. One important application of the Monte Carlo simulation is to predict the spatial dose distribution in the target geometry. For simplification, a mathematical model of a human body is usually used as the target, but only the average dose over the whole organ or tissue can be obtained rather than the accurate spatial dose distribution. In this research, we developed a method using MATLAB to convert the medical images of a patient from CT scanning into the patient voxel geometry. Hence, if the patient voxel geometry is used as the target in the Monte Carlo simulation, the accurate spatial dose distribution in the target can be obtained. A data analysis tool?root was used to score the simulation results during a Geant4 simulation and to analyze the data and plot results after simulation. Finally, we successfully obtained the accurate spatial dose distribution in part of a human body after treating a patient with prostate cancer using proton therapy.

Thesis (Ph.D. - Hydrology and Water Resources)--University of Arizona. / Includes bibliographical references (leaves 332-356).

Professional Master's Project in partial fulfillment of the requirements for the degree of Master of Science (M.S.)--Worcester Polytechnic Institute. / Keywords: first-to-default baskets; multi-name credit derivatives; copula functions. Includes bibliographical references (leaf 29 ).