Abstract
The fast developments and ongoing demands in radiation dosimetry have piqued the attention of many software developers and physicists to create powerful tools to make their experiments more exact, less expensive, more focused, and with a wider range of possibilities. Many software toolkits, packages, and programs have been produced in recent years, with the majority of them available as open source, open access, or closed source. This study is mostly focused to present what are the Monte Carlo software developed over the years, their implementation in radiation treatment, radiation dosimetry, nuclear detector design for diagnostic imaging, radiation shielding design and radiation protection. Ten software toolkits are introduced, a table with main characteristics and information is presented in order to make someone entering the field of computational Physics with Monte Carlo, make a decision of which software to use for their experimental needs. The possibilities that this software can provide us with allow us to design anything from an X-Ray Tube to whole LINAC costly systems with readily changeable features. From basic x-ray and pair detectors to whole PET, SPECT, CT systems which can be evaluated, validated and configured in order to test new ideas. Calculating doses in patients allows us to quickly acquire, from dosimetry estimates with various sources and isotopes, in various materials, to actual radiation therapies such as Brachytherapy and Proton therapy. We can also manage and simulate Treatment Planning Systems with a variety of characteristics and develop a highly exact approach that actual patients will find useful and enlightening. Shielding is an important feature not only to protect people from radiation in places like nuclear power plants, nuclear medical imaging, and CT and X-Ray examination rooms, but also to prepare and safeguard humanity for interstellar travel and space station missions. This research looks at the computational software that has been available in many applications up to now, with an emphasis on Radiation Dosimetry and its relevance in today's environment.
Highlights
The Monte Carlo technique uses random numbers as a reference point to simulate a given circumstance
In this work we have presented all of the most known software packages that can do experimentation simulation, combining high energy physics, algorithms and statistics
We believe that a huge role in the near future for Monte Carlo is the analysis of algorithms and new modelling to existing problems and new building, supporting and maintaining software tools
Summary
The Monte Carlo technique uses random numbers as a reference point to simulate a given circumstance. The physical process may be directly reproduced in most Monte Carlo applications. All that is required is that the system and physical processes can be described using established probability density functions. Random sampling may be used to simulate these probability density functions if they are precisely specified. Simulation studies, in general, provide a number of benefits over experimental research. It is quite simple to alter various criteria in any given model and analyse the impact of those alternations on the system’s efficiency. To gain in rate of convergence, one must lose in reliability, which means that the enhanced rate of convergence is compensated by tolerating some degree of uncertainty in the findings.
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