Exploring the gamma surface: A new method for visualising modulated radiotherapy quality assurance results. (October 2020)

• Record Type: Journal Article
• Authors: Peet, Samuel C.; Yu, Liting; Maxwell, Sarah; Crowe, Scott B.; Trapp, Jamie V.; Kairn, Tanya
• Is Part Of: Physica medica. Volume 78(2020)
• Page Start: 166
• Page End: 172
• Publication Date: 2020-10
• Subjects: IMRT -- Quality assurance -- Gamma analysis; Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2020.09.021
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: Gamma surface plots feature rich information describing levels of agreement. These plots are useful for investigating borderline results and failures. They have the potential to spotlight errors that may go unnoticed in other analyses. 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.