Comparison of RayStation Collapsed Cone Convolution and Monte Carlo Algorithms in Calculation of Treatment Dose to Base-of-Skull Metastases

<p dir="ltr">For radiotherapy treatment planning, type-B algorithms such as collapsed cone convolution have been the clinical standard due to their fast calculation compared to type-C algorithms such as Monte Carlo. As computational speeds increase, type-C algorithms have become increasingly clinically viable, and the advent of software such as RayStation that uses Monte Carlo means that it’s important to create a base of research to understand how these types of algorithms calculate dose differently. Although disadvantages of type-B algorithms are known, one being that they become inaccurate when considering the presence of inhomogeneous tissue such as bone, research is needed for clearer evidence as to which type of algorithm will be best in a clinical setting.</p><p dir="ltr">In this thesis, RayStation’s Monte Carlo (type-C) and collapsed cone (type-B) algorithms were used in the calculation of dose in a treatment targeting a tumour partially covering the base of the skull in the occipital bone of a patient. Collapsed cone estimates dose analytically and transports particles rectilinearly, while Monte Carlo uses pseudorandom number generation to simulate particle interactions at given intervals. For each algorithm, three beam plans were considered: 4-beam IMRT, 5-beam IMRT and 2-arc VMAT. Additionally, there was alternation between using material override for different regions of the patient head such as the compact bone, soft bone and airways, and no material override. What was observed was that, for IMRT plans, Monte Carlo generally provided a slightly higher estimation of dose to the planning target volume compared to collapsed cone, while also more accurately characterising the reduced dose to bone. In comparison, VMAT plans showed negligible differences between the use of algorithms. This suggests that the collapsed cone algorithm does not describe dose with inhomogeneous tissue as effectively, though this difference becomes more trivial with VMAT plan. Further research can better clarify this difference with the use of a golden standard model such as EGSnrc or Geant4’s Monte Carlo algorithms.</p>