ESTRO 37 Abstract book

S40

ESTRO 37

Results The level of quenching at the Bragg peak varies from 80%, 36%, 22%, 15%, and 13% for the corresponding proton beam energies 85.6, 100.9, 124, 144.9, and 161.6 MeV (figure 1). The calculated light using Birks scintillation model fits well compared to the measured light to within 5% for all proton beam energies, except for the lowest beam energy (85.6 MeV), which resulted in over 10% difference around the Bragg peak. Conclusion A quenching correction factor can be extracted from Birks quenching parameter and applied to the measured light to determine absorbed dose for proton beams. Further improvements and modifications to the existing ionization quenching models are warranted to improve the agreements especially at the lower energies.

Figure 1 . (Top) Central axis depth-dose profiles for the Monte Carlo simulated dose, measured light, and light calculated using the Birks model. (Bottom) The ratio of the calculated light to the measured light (L c /L m ) for the five energies.

OC-0083 Light yield and ionization quenching measurements of the 3D scintillator detector for proton therapy F. Alsanea 1 , C. Darne 1 , D. Robertson 2 , S. Beddar 1 1 U.T. M.D. Anderson Cancer Center, Radiation Physics, Houston- TX, USA 2 Mayo Clinic Arizona, Radiation Oncology, Phoenix- AZ, USA Purpose or Objective Ionization quenching is a known phenomenon that causes non-linear scintillation response to heavy charged particles with high ionizing radiation density. Therefore, an ionization quenching correction factor must be applied to the detector to measure absorbed dose. In this work, we measure the relative light yield of a newly developed three-dimensional (3D) scintillator detector and determine the ionization quenching for spot scanning proton beams. Material and Methods We have developed a large-volume liquid scintillator (LS) detector to measure the dose distributions for spot scanning proton beams (20 cm x 20 cm x 20 cm). The new LS system can measure profiles of spot scanning proton beams in real time and has sub-millimeter spatial resolution. The scintillation light is collected by a three camera system, each consisting of an objective lens and a scientific-CMOS camera. We have exposed our detector to five different proton beam energies produced by the synchrotron at UT M.D. Anderson Cancer Center Proton Therapy Center (85.6, 100.9, 124, 144.9, and 161.6 MeV). We used Monte Carlo simulations to obtain the dose and linear energy transfer (LET) for these beam energies. Only one axial projection was used to generate integrated depth dose curves (the same quenching correction would apply on all 3 cameras). We compared the light emission to the dose calculated by the Monte Carlo simulation and applied a Birks scintillation model to fit the measured light.

Proffered Papers: PHY 2: Treatment planning in particle therapy

OC-0084 A novel method to estimate mean excitation energies and their uncertainties for particle therapy E. Baer 1,2 , P. Andreo 3 , A. Lalonde 4 , G. Royle 1 , H. Bouchard 4 1 University College London, Medical Physics and Biomedical Engineering, London, United Kingdom 2 National Physical Laboratory, Acoustics and Ionising Radiation Team, Teddington, United Kingdom 3 Stockholm University and Karolinska University Hospital, Department of Medical Radiation Physics, Stockholm, Sweden 4 Université de Montréal, Department of Physics, Montréal, Canada Purpose or Objective Methods to precisely determine elemental compositions using for example dual-energy computed tomography exist and can be used as input data for particle therapy planning in the near future. Uncertainties arise from elemental mean excitation energies ( I -values). Currently used I -values were proposed in 1981, and no thorough uncertainty budget exists. The aim of this study is to revisit elemental I -values for clinical particle therapy planning and establish an uncertainty budget for tissue relative stopping powers (RSPs) and particle range arising from I -values and the Bragg additivity rule. Material and Methods We propose a method to optimize elemental I -values for the use in compounds, by using measured I -values of different materials (quoted in ICRU 37) and least square optimization. We include recent literature on measured