ESTRO 2022 - Abstract Book

S343

Abstract book

ESTRO 2022

Figure 1 - a) Dose distribution (Gy) on the rCT. b), c), d), and e) Dose differences (% of prescription dose) vs. a) for methods 1, 2, 3, and 4 respectively. Positive values indicate an underdose relative to a). Conclusion All methods were considered clinically viable. Method 4, the machine learning method, was found to be most suitable for clinical implementation due to its high dosimetric accuracy and short generation time. Further investigation is required to assess these methods in situations where the CBCT and CT are significantly different and for other anatomical sites.

PD-0402 Impact of CBCT-based patient positioning uncertainty due to the ROI/DOF selection for proton therapy

M. Guo 1,2,3 , E. Batin 1 , A. Bolsi 1 , S. Safai 1 , D. Weber 1,4,5 , A. Lomax 1,6 , Z. Chen 2 , Y. Zhang 1

1 Paul Scherrer Institute, Center for Proton Therapy, Villigen-PSI, Switzerland; 2 Chinese Academy of Sciences, Shanghai Institute of Applied Physics, Shanghai, China; 3 University of Chinese Academy of Sciences, Nuclear Technology and Application, Beijing, China; 4 University Hospital Zurich, Department of Radiation Oncology, Zurich, Switzerland; 5 University Hospital Bern, Department of Radiation Oncology, Bern, Switzerland; 6 ETH Zurich, Department of Physics, Zurich, Switzerland Purpose or Objective When rigidly registering daily Cone Beam CT (CBCT) to the planning CT (pCT), the derived positioning offsets will be dependent on the Region of Interest (ROI) and/or Degree of Freedom (DOF) selected. We aim to investigate the geometric and dosimetric impact of ROI/DOF selection for proton treatments of skull-based and Head-and-Neck (H&N) tumours.

Materials and Methods