Browsing by Subject "Monte Carlo Method"
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Item Development of Monte Carlo Treatment Planning and Dosimetry System for Small Animal Irradiator(2012-12-27) Pidikiti, Rajesh 1982-; Stojadinovic, Strahinja; Solberg, Timothy; Anderson, Jon; Foster, Ryan; Medin, PaulThis work utilizes Monte Carlo simulation techniques to build a model of an x-ray tube in order to develop Monte Carlo treatment planning system for a small animal irradiator. To accomplish this, the absolute dose calibration of the irradiator performed in accordance with the recommendations of AAPM TG-61 protocol. Both in-air and in-water calibrations were performed at a 30.5 cm source-to-surface distance (SSD) for the reference applicator 40x40 mm2 square field size. The BEAM/EGS was used to model 225 kV photon beams from a small animal irradiator (Precision XRAD225). The Monte Carlo model was extensively tuned to provide good agreement with achievable measurements of the beam characteristics (e.g. PDD and off-axis ratios). Subsequently, output factors for various square and circular applicators were measured using different dosimeters (ionization chamber, radiochromic film) and compared with MC simulations. The standard gamma index method with AAPM TG 53 recommendations are used to benchmark the measurements (radio chromic film) against planar dose (Monte Carlo simulation) along with isodose lines and profiles in both homogeneous and heterogeneous mediums. The statistical uncertainty on the MC-calculated results is between 0.5% and 2% for most points. The CBCT images obtained on the XRAD 225Cx irradiator were converted to a material /density matrix as an input to DOSXYZnrc a MC dose computation module. The measured and computed point doses and isodose distributions were compared using the gamma index method. The absolute dose measured for reference collimator at 30.5 cm SSD in water and in air is 3.42 and 3.45 Gy/min. The agreement between simulated and measured dosimetric characteristics was excellent. For all fields, a good agreement is observed between measurements and calculations. Finally, a Monte Carlo treatment planning system for heterogeneous media is developed and validated. Monte Carlo simulation provides an indispensible tool for validating measurements of the smallest field sizes used in preclinical small animal irradiation.Item A New Approach to Optimize a Protein Energy Function on a Folding Pathway Using Gō-Like Potential and All-Atom, Ab Initio Monte Carlo Simulations(2016-01-19) Safronova, Aleksandra; Goldsmith, Elizabeth J.; Grishin, Nick V.; Otwinowski, Zbyszek; Rice, Luke M.Prediction of a protein structure is important for understanding the function of a protein. The process of protein structure prediction employs the approximation of a protein free energy that guides protein folding to the protein's native state. A function with a good approximation of the protein free energy should allow estimation of the structural distance of the evaluated candidate structure to the protein native state. Currently the energy optimization process relies on the correlation between the energy and the similarity to the native structure. The energy function is presented as a weighted sum of components which are designed by human experts with the use of statistical analysis of solved protein strictures. Values of the weights are derived through the procedure that maximizes the correlation between the energy and the similarity to the native structure measured by a root mean square deviation between coordinates of the protein backbone. Two major components are required for a successful ab initio modelling: (1) an effective energy function that discriminates the native protein structure out of all possible decoy structures; (2) an efficient sampling algorithm that quickly searches for the low-energy states. In this dissertation a new method for energy optimization is proposed. The method relies on a fast sampling algorithm and targets successful protein folding. The weights for energy components are optimized on a found with the Gō potential energy fast folding pathway. The Lennard-Jones potential, the Lazaridis-Karplus solvation potential, hydrogen bonding potential are used in the optimization algorithm. The optimized weights successfully predict all α and α/β proteins. The proposed strategy is conceptually different from the existing methods that optimize the energy on solved protein structures. The developed algorithm is a novel concept that allows the optimization of a more complex functional combination of the energy components that would improve the prediction quality.