ESTRO 2023 - Abstract Book

S182

Saturday 13 May

ESTRO 2023

PD-0247 Clinical suitability of CBCT-based synthetic CTs in proton therapy for head and neck patients R. de Koster 1 , A. Thummerer 1 , G. Guterres Marmitt 1 , D. Scandurra 1 , H. Langendijk 1 , S. Both 1 1 University Medical Center Groningen (UMCG), Radiation Oncology, Groningen, The Netherlands Purpose or Objective In adaptive proton therapy workflows, robustness is often monitored using weekly repeat CTs (rCTs). Alongside the rCTs, daily in-room Cone-Beam CT (CBCT) images are acquired for pre-treatment position verification. While CBCT images are not suitable for direct proton dose calculations, as they suffer from severe imaging artifacts, they can be converted into higher quality synthetic CT (sCT) images using neural networks. The image quality of CBCT-based sCTs has previously been shown as similar to rCT quality, but the dosimetric accuracy of sCTs in a clinical setting is yet to be determined. The aim of this work was to compare the daily sCT images, generated by a neural network, to the planning CT (pCT) and rCTs of head and neck (HN) cancer patients to investigate the dosimetric accuracy of CBCT-based sCTs, towards treatment robustness evaluations and online adaptive proton therapy workflows. Materials and Methods A dataset of 57 HN cancer patients previously treated with proton therapy at our center was used to generate synthetic CT images from daily CBCTs, using a previously developed and trained UNet-like deep convolutional neural network. The clinical treatment plans of the patients were used to recalculate the proton dose on the weekly rCTs and daily sCTs. The