Evolution of Computational Techniques Implementing Monte Carlo in Radiotherapy | Abstract
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Evolution of Computational Techniques Implementing Monte Carlo in Radiotherapy

Author(s): Poonam Yadav and Velayudham Ramasubramanian3

The concept of Monte Carlo has shown its impact on almost all segments of not just science but even in management and arts. Since its evolution in 1950’s, it has grown surprisingly to assist every field. Monte Carlo based methods works through random number generation and uses several techniques for statistical tests of pseudo numbers. Most of the applications of Monte Carlo come under the generic application category; also it has assisted the radiation physics a lot, to make radiation oncology well equipped with techniques for resolving complicated results of treatment planning systems (TPS) and new techniques emerging in radiotherapy. At present various codes like BEAM, EGSnrc, FLUKA, MCNPX and GEANT4 are based on random numbers. The programming based on these codes solves the problems arising from machine simulation to dose distribution. Its contribution to radiological and medical physics has been substantial and cannot be underestimated; also Monte Carlo has its own added advantages in computational techniques, especially the Monte Carlo N particle transport code (MCNP) plays a significant role in simulation of complex geometries and calculation of radiation dose by simulating behavior of subatomic particles. It is now a day not an accessory but has turned out to be an essential prerequisite for radiation physicists, as it is universally accepted that Monte Carlo is required for better measurement of dose distribution for machines. It can be concluded that the development of Monte Carlo technique did not follow any incremental approach but it tends to be evolved one. The latest developments in the Monte Carlo have scientifically proved and validated numerous models of newly emerging modalities like TomoTherapy and others. Apart from this, it is as necessary to train scholars in this crucial field as ongoing developments in emerging trends of Monte Carlo Method.