Quantification of deformable image registration uncertainties for dose accumulation on head and neck cancer proton treatments

Purpose: Head and neck cancer (HNC) patients in radiotherapy require adaptive treatment plans due to anatomical changes. Deformable image registration (DIR) is used in adaptive radiotherapy, e.g. for deformable dose accumulation (DDA). However, DIR's ill-posedness necessitates addressing uncertainties, often overlooked in clinical implementations. DIR's further clinical implementation is hindered by missing quantitative commissioning and quality assurance tools. This study evaluates one pathway for more quantitative DDA uncertainties. Methods: For five HNC patients, each with multiple repeated CTs acquired during treatment, a simultaneousintegrated boost (SIB) plan was optimized. Recalculated doses were warped individually using multiple DIRs from repeated to reference CTs, and voxel-by-voxel dose ranges determined an error-bar for DDA. Followed by evaluating, a previously proposed early-stage DDA uncertainty estimation method tested for lung cancer, which combines geometric DIR uncertainties, dose gradients and their directional dependence, in the context of HNC. Results: Applying multiple DIRs show dose differences, pronounced in high dose gradient regions. The patient with largest anatomical changes (-13.1 % in ROI body volume), exhibited 33 % maximum uncertainty in contralateral parotid, with 54 % of voxels presenting an uncertainty >5 %. Accumulation over multiple CTs partially mitigated uncertainties. The estimation approach predicted 92.6 % of voxels within +/- 5 % to the reference dose uncertainty across all patients. Conclusions: DIR variations impact accumulated doses, emphasizing DDA uncertainty quantification's importance for HNC patients. Multiple DIR dose warping aids in quantifying DDA uncertainties. An estimation approach previously described for lung cancer was successfully validated for HNC, for SIB plans, presenting different dose gradients, and for accumulated treatments.