ESTRO 2022 - Abstract Book

S549

Abstract book

ESTRO 2022

Conclusion We implemented a calculation of D bio in FLUKA MC using different OER estimations and variable RBE calculations in order to perform hypoxia adaptation of proton therapy plans. Application and comparison of the models showed overall good agreement between different models, although some variation in LET sensitivity was observed. With reliable hypoxia information, applications of OER adapted/adapting RBE models could become a useful tool for treatment plan evaluation and optimization.

OC-0622 Universal and dynamic ridge filter for pencil beam scanning (PBS) proton therapy

V. Maradia 1,2 , I. Colizzi 1,2 , A. Savenije 1,3 , D. Meer 1 , J.M. Schippers 4 , D.C. Weber 1,5,6 , A.J. Lomax 1,2 , O. Actis 1 , S. Psoroulas 1

1 Paul Scherrer Institute, Center for Proton Therapy, Villigen, Switzerland; 2 ETH Zurich, Department of Physics, Zurich, Switzerland; 3 TU Delft, Department of Physics, Delft, The Netherlands; 4 Paul Scherrer Institute, Larger Accelerator facility , Villigen, Switzerland; 5 University of Zurich, University Hospital Zurich, Zurich, Switzerland; 6 University of Bern, University Hospital Bern, Bern, Switzerland Purpose or Objective Our objective is to design a universal dynamic energy modulator (ridge filter) for PBS proton therapy. By using a ridge filter, the Bragg peak is broadened, thus lowering the number of energies required for homogeneous target coverage. Treatment delivery time in PBS proton therapy depends on beam-on time and the dead time (time required to change energy layers and/or lateral position). By lowering the number of required energies, we want to reduce the treatment delivery time. Materials and Methods To reduce the number of energy layers, we developed a new energy modulation unit that comprises two identical ridge filters placed just before the isocenter (shown in Figure 1). Both ridge filters are movable relative to each other to change the Bragg peak’s characteristics dynamically. For predicting the Bragg peak shape with the ridge filter, we used Monte Carlo simulations implemented in TOPAS with the beam model of PSI Gantry 2. To compare the reduction in energy layers with ridge filter, we generated eight different spread out Bragg peaks (SOBPs) with a thickness range from 4 to 11 cm with and without the ridge filter. Results Figure 1 shows that a maximum broadening of the Bragg peak is achieved (almost 2 cm SOBP) if the filter is aligned, peaks with peaks (figure 1(a)). However, if the filter is aligned peak with valley (figure 1(b)), the ridge filter acts as a range shifter. Figures 1(c) show an 11 cm wide SOBP generated by the ridge filter while aligning peak with valley for the first two high-energy beams (to get the sharper fall-off) and for the remaining lower energies, the filter is aligned in peak-to-peak position (to maximize the Bragg peak broadening). In this way, the dynamic ridge filter produces a sharper distal fall-off compared to a normal ridge filter. Table 1 shows the number of energy layers required to generate different widths of SOBP with and without ridge filter. With the dynamic ridge filter, we managed to reduce the required energy layers by a factor of three compared to a SOBP generated without a ridge filter.