SU-E-J-145: Validation of An Analytical Model for in Vivo Range Verification Using GATE Monte Carlo Simulation in Proton Therapy

2015 ◽  
Vol 42 (6Part9) ◽  
pp. 3297-3298
Author(s):  
C Lee ◽  
H Lin ◽  
T Chao ◽  
I Hsiao ◽  
K Chuang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Analytical Model ◽  
Proton Therapy ◽  
Range Verification

scholarly journals Cheating at Craps

2021 ◽  
Vol 48 (4) ◽  
pp. 53-61
Author(s):  
Andrea Marin ◽  
Carey Williamson
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Modeling And Simulation ◽  
Analytical Model ◽  
Quantitative Evaluation ◽  
Mathematical Analysis ◽  
Simulation Modeling ◽  
Analytical Modeling ◽  
The World ◽  
Simulation Results

Craps is a simple dice game that is popular in casinos around the world. While the rules for Craps, and its mathematical analysis, are reasonably straightforward, this paper instead focuses on the best ways to cheat at Craps, by using loaded (biased) dice. We use both analytical modeling and simulation modeling to study this intriguing dice game. Our modeling results show that biasing a die away from the value 1 or towards the value 5 lead to the best (and least detectable) cheating strategies, and that modest bias on two loaded dice can increase the winning probability above 50%. Our Monte Carlo simulation results provide validation for our analytical model, and also facilitate the quantitative evaluation of other scenarios, such as heterogeneous or correlated dice.

scholarly journals [OA051] Toward a new treatment planning system accounting for in-vivo proton range verification in proton therapy

2018 ◽  
Vol 52 ◽  
pp. 21
Author(s):  
Liheng Tian ◽  
Georgios Dedes ◽  
Guillaume Landry ◽  
Florian Kamp ◽  
Katharina Niepel ◽  
...  
Keyword(s):  
Treatment Planning ◽  
Proton Therapy ◽  
Planning System ◽  
Treatment Planning System ◽  
New Treatment ◽  
Range Verification

Study of the Influence of Radionuclide Biokinetics on the Efficiency of In Vivo Counting Using Monte Carlo Simulation

2009 ◽  
Vol 96 (5) ◽  
pp. 558-567 ◽  
Author(s):  
Stephanie Lamart ◽  
Eric Blanchardon ◽  
Andrey Molokanov ◽  
Gary H. Kramer ◽  
David Broggio ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo

scholarly journals Comparison of knife-edge and multi-slit camera for proton beam range verification by Monte Carlo simulation

2019 ◽  
Vol 51 (2) ◽  
pp. 533-538 ◽  
Author(s):  
Jong Hoon Park ◽  
Sung Hun Kim ◽  
Youngmo Ku ◽  
Hyun Su Lee ◽  
Chan Hyeong Kim ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Proton Beam ◽  
Knife Edge ◽  
Beam Range ◽  
Range Verification

Secondary Fluorescence of 3D Heterogeneous Materials Using a Hybrid Model

2020 ◽  
Vol 26 (3) ◽  
pp. 484-496
Author(s):  
Yu Yuan ◽  
Hendrix Demers ◽  
Xianglong Wang ◽  
Raynald Gauvin
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Analytical Model ◽  
Hybrid Model ◽  
Three Dimensional ◽  
Heterogeneous Materials ◽  
X Ray ◽  
The Monte Carlo Method ◽  
Secondary Fluorescence ◽  
Good Agreement

AbstractIn electron probe microanalysis or scanning electron microscopy, the Monte Carlo method is widely used for modeling electron transport within specimens and calculating X-ray spectra. For an accurate simulation, the calculation of secondary fluorescence (SF) is necessary, especially for samples with complex geometries. In this study, we developed a program, using a hybrid model that combines the Monte Carlo simulation with an analytical model, to perform SF correction for three-dimensional (3D) heterogeneous materials. The Monte Carlo simulation is performed using MC X-ray, a Monte Carlo program, to obtain the 3D primary X-ray distribution, which becomes the input of the analytical model. The voxel-based calculation of MC X-ray enables the model to be applicable to arbitrary samples. We demonstrate the derivation of the analytical model in detail and present the 3D X-ray distributions for both primary and secondary fluorescence to illustrate the capability of our program. Examples for non-diffusion couples and spherical inclusions inside matrices are shown. The results of our program are compared with experimental data from references and with results from other Monte Carlo codes. They are found to be in good agreement.

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