Abstract

In the literature, most of the scientific studies in the field of medical physics have used Monte Carlo (MC) techniques for the evaluation and verification of methods and algorithms, the design of new systems, and the quantification of new tracers in imaging or in dosimetry. The basis of MC numerical techniques can be described as statistical methods where random number generators are used to perform more realistic simulations for specified situations. Hence, the importance of such simulation programs is necessary in understanding the underlying physical processes that are taking place in a clinical procedure. The most important aspect of the application of MC simulations in the field of medical physics is the simplicity of the calculations of the physical interactions that are taking place (e.g., very-low-energy photon interactions, production of secondary charged particles, photon elastic scattering, electron ionization, and attenuated particles). Alongside, a critical aspect regarding the daily application of MC methods in hospital and institutional workstations is the consumption of time and resources for the acquisition of such procedures. Realistic simulations for clinical practice still require large computing resources. Thus, over the last 20 years, computer science has been rapidly evolving by enabling high-performance computers (HPC), clusters and grids of central processing units, and lately graphical physical units (GPUs) that are extensively used for the acquisition of MC simulations.