Samuel C Peet1, Liting Yu2, Sarah Maxwell3, Scott B Crowe2, Jamie V Trapp4, Tanya Kairn2. 1. Royal Brisbane and Women's Hospital, Herston, QLD 4029, Australia; Queensland University of Technology, Brisbane, QLD 4001, Australia. Electronic address: samuel.peet.physics@gmail.com. 2. Royal Brisbane and Women's Hospital, Herston, QLD 4029, Australia; Queensland University of Technology, Brisbane, QLD 4001, Australia. 3. Royal Brisbane and Women's Hospital, Herston, QLD 4029, Australia. 4. Queensland University of Technology, Brisbane, QLD 4001, Australia.
Abstract
PURPOSE: This work presents a novel method of visualising the results of patient-specific quality assurance (QA) for modulated radiotherapy treatment plans, using a three-dimensional distribution of gamma pass rates, referred to as the "gamma surface". The method was developed to aid in comparing borderline and failing QA plans, and to better compare patient-specific QA results between departments. METHODS: Gamma surface plots were created for a representative sample of situations encountered during patient-specific QA. To produce a gamma surface plot, for each QA result, gamma pass rates were plotted as a heat map, with dose difference on one axis and distance-to-agreement on the other. This involved the calculation of 100 × 100 gamma pass rates over a dose difference and distance-to-agreement grid. As examples, five 220 × 680 arrays of dose points from radiotherapy treatment plans were compared against measurement data consisting of 21 × 66 arrays of dose points spaced 10 mm apart. RESULTS: The gamma surface plots facilitated the rapid evaluation of criteria combinations for each plan, clearly highlighting the difference between plans that are modelled and delivered well, and those that are not. Large scale features were also evident in each surface, hinting at potential over-modulation, systematic dose errors, and small or large scale areas of disagreement in the distributions. CONCLUSIONS: Gamma surface plots are a useful tool for investigating QA failures and borderline results, and have the capacity to grant insights into treatment plan QA performance that may otherwise be missed.
PURPOSE: This work presents a novel method of visualising the results of patient-specific quality assurance (QA) for modulated radiotherapy treatment plans, using a three-dimensional distribution of gamma pass rates, referred to as the "gamma surface". The method was developed to aid in comparing borderline and failing QA plans, and to better compare patient-specific QA results between departments. METHODS: Gamma surface plots were created for a representative sample of situations encountered during patient-specific QA. To produce a gamma surface plot, for each QA result, gamma pass rates were plotted as a heat map, with dose difference on one axis and distance-to-agreement on the other. This involved the calculation of 100 × 100 gamma pass rates over a dose difference and distance-to-agreement grid. As examples, five 220 × 680 arrays of dose points from radiotherapy treatment plans were compared against measurement data consisting of 21 × 66 arrays of dose points spaced 10 mm apart. RESULTS: The gamma surface plots facilitated the rapid evaluation of criteria combinations for each plan, clearly highlighting the difference between plans that are modelled and delivered well, and those that are not. Large scale features were also evident in each surface, hinting at potential over-modulation, systematic dose errors, and small or large scale areas of disagreement in the distributions. CONCLUSIONS: Gamma surface plots are a useful tool for investigating QA failures and borderline results, and have the capacity to grant insights into treatment plan QA performance that may otherwise be missed.