Evaluation of the dose perturbation around gold and steel fiducial markers in a medical linac through Geant4 Monte Carlo simulation

2015 ◽  
Vol 23 (2) ◽  
pp. 135-140 ◽  
Author(s):  
Antonio Pontoriero ◽  
Ernesto Amato ◽  
Giuseppe Iatí ◽  
Costantino De Renzis ◽  
Stefano Pergolizzi
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Fiducial Markers ◽  
Dose Perturbation ◽  
Geant4 Monte Carlo Simulation ◽  
Medical Linac

Monte Carlo Simulation of a 18 MV Medical Linac Photon Beam Using GATE/GEANT4

2021 ◽  
Vol 76 (1) ◽  
pp. 15-21
Author(s):  
M. Fiak ◽  
A. Fathi ◽  
J. Inchaouh ◽  
A. Khouaja ◽  
A. Benider ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Photon Beam ◽  
Medical Linac

scholarly journals High Precision Positioning with Multi-Camera Setups: Adaptive Kalman Fusion Algorithm for Fiducial Markers

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2746 ◽  
Author(s):  
Dragos Constantin Popescu ◽  
Ioan Dumitrache ◽  
Simona Iuliana Caramihai ◽  
Mihail Octavian Cernaianu
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
High Precision ◽  
Measurement Noise ◽  
Fusion Method ◽  
Fiducial Markers ◽  
Multiple Cameras ◽  
Precision Positioning ◽  
Fusion Algorithm ◽  
Physics Experiments

The paper addresses the problem of fusing the measurements from multiple cameras in order to estimate the position of fiducial markers. The objectives are to increase the precision and to extend the working area of the system. The proposed fusion method employs an adaptive Kalman algorithm which is used for calibrating the setup of cameras as well as for estimating the pose of the marker. Special measures are taken in order to mitigate the effect of the measurement noise. The proposed method is further tested in different scenarios using a Monte Carlo simulation, whose qualitative precision results are determined and compared. The solution is designed for specific positioning and alignment tasks in physics experiments, but also, has a degree of generality that makes it suitable for a wider range of applications.

Track structure modeling in liquid water: A review of the Geant4-DNA very low energy extension of the Geant4 Monte Carlo simulation toolkit

2015 ◽  
Vol 31 (8) ◽  
pp. 861-874 ◽  
Author(s):  
M.A. Bernal ◽  
M.C. Bordage ◽  
J.M.C. Brown ◽  
M. Davídková ◽  
E. Delage ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Liquid Water ◽  
Low Energy ◽  
Structure Modeling ◽  
Track Structure ◽  
Geant4 Monte Carlo Simulation

scholarly journals Radiation protection study for a reloading operation of cobalt 60 sources in the Moroccan ionisation station using the GEANT4 Monte-Carlo simulation code

2021 ◽  
Vol 234 ◽  
pp. 00007
Author(s):  
Adil Aknouch ◽  
Youssef El-ouardi ◽  
Mohammed Mouhib ◽  
Rajaa Sebihi ◽  
Abdelmajid Choukri
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Radiation Protection ◽  
Agricultural Research ◽  
Simulation Code ◽  
The Public ◽  
The Monte Carlo Method ◽  
High Doses ◽  
In The Beginning ◽  
Geant4 Monte Carlo Simulation

The operation of reloading the irradiators is considered among the tasks requiring high radiation protection monitoring, to protect the intervening manipulators, the public and the environment. Morocco is among the countries that have a cobalt irradiator, installed at the National Institute of Agricultural Research (NIAR) of Tangier, to carry out research in the field of agronomy. In the beginning, the irradiator used low doses of activity for the study of products only, for treatment of high doses. The NIAR carried out a reload to increase the activity. To perform this, a temporary pool was installed inside the irradiation room to handle the sources safely. A radiation protection study is necessary to ensure the safe operation. This operation requires a height level of exposure. To ovoid the exposer risk, it is proposed to use the Monte Carlo method thanks to its reliability in the dosimetric calculation. This article presents a radiation protection study of the Moroccan irradiator reloading operation using the GEANT4 Monte-Carlo Simulation Code.

scholarly journals 3D convolutional GAN for fast simulation

2019 ◽  
Vol 214 ◽  
pp. 02010 ◽  
Author(s):  
Sofia Vallecorsa ◽  
Federico Carminati ◽  
Gulrukh Khattak
Keyword(s):  
Machine Learning ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Three Dimensional ◽  
Machine Learning Techniques ◽  
Generative Adversarial Networks ◽  
Detector Response ◽  
Fast Simulation ◽  
Geant4 Monte Carlo Simulation ◽  
Detector Simulation

Machine Learning techniques have been used in different applications by the HEP community: in this talk, we discuss the case of detector simulation. The need for simulated events, expected in the future for LHC experiments and their High Luminosity upgrades, is increasing dramatically and requires new fast simulation solutions. We describe an R&D activity aimed at providing a configurable tool capable of training a neural network to reproduce the detector response and speed-up standard Monte Carlo simulation. This represents a generic approach in the sense that such a network could be designed and trained to simulate any kind of detector and, eventually, the whole data processing chain in order to get, directly in one step, the final reconstructed quantities, in just a small fraction of time. We present the first application of three-dimensional convolutional Generative Adversarial Networks to the simulation of high granularity electromagnetic calorimeters. We describe detailed validation studies comparing our results to Geant4 Monte Carlo simulation. Finally we show how this tool could be generalized to describe a whole class of calorimeters, opening the way to a generic machine learning based fast simulation approach.

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