## 2 Materials and Methods  
### Digitally Enhanced Phantom  
In this study, the ATOM Max Dental and Diagnostic Head Phantom Model-711 (CIRS, Norfolk, VA, USA) (HN phantom) was used. The HN phantom approximates to the average male human head in both size and structure. The original phantom includes detailed 3D anthropomorphic anatomy, such as the brain, bone, larynx, trachea, sinus, nasal cavities and teeth. The bones contain both cortical and trabecular components. The teeth include the distinct dentine, enamel and root structures, including the nerves. The sinus cavities are fully open. The HN phantom was scanned with both CT and CBCT. The CT image datasets were acquired using a GE Hi-Speed (General Electric Company, Boston, MA, USA) scanner, with the acquisition parameters set in agreement with our clinical HN protocol (120 KV, 100 mA, 2 mm slice thickness). As a second step, the CBCT image sets were acquired on a TrueBeam STx linear accelerator (Varian Medical Systems, Palo Alto, CA, USA), using our HN image acquisition protocols (HN modality). To acquire the entire head of the phantom, a multiscan CBCT was performed, and the data were combined using the TrueBeam STx advanced reconstruction module. For the CBCT images, the linear attenuation to the Hounsfield Unit (HU) values calibration was done using scans of the Catphan 504 phantom, following the Varian TrueBeam acceptance procedure manual [23], by assigning the HU values from the Catphan manual to the reconstructed raw image values of the seven material inserts, followed by a linear fit. Both the CT and CBCT image sets were post-processed to digitally insert several OARs which are not present in the original phantom, including the brainstem, oral cavity, left and right parotid glands, larynx and eyes. Firstly, a radiation oncologist (MG) drew outlines of these anatomical structures on the phantom CT and CBCT images, following international guidelines [22], for their standard shape, anatomical position and volume. Subsequently, the original HU values were replaced with surrogate HU values for all the above-mentioned structures, using an in-house developed script based on the MATLAB code (The Mathworks, Natick, MA, USA) and the Computational Environment for Radiological Research tool (CERR, [[http://www.github.com/cerr/CERR](http://www.github.com/cerr/CERR)]([http://www.github.com/cerr/CERR](http://www.github.com/cerr/CERR)), accessed on 20 February 2020) [24]. The surrogate HU value in each voxel, \(i\), of a given organ OAR, \(HU_{processed\ OAR_{i}}^{phantom}\), was derived using the following equation:  
\[HU_{processed\ OAR_{i}}^{phantom}=\left(\overline{HU_{OAR}^{pt}}-\overline{ HU_{l}^{pt}}\right)+HU_{original\ OAR_{i}}^{phantom}, \tag{1}\]  
where \(\overline{HU_{OAR}^{pt}}\) and \(\overline{HU_{l}^{pt}}\) are the mean HU values over the voxels included in a ROI belonging to a specific organ (\(\overline{HU_{OAR}^{pt}}\)) and the tissue around that organ (\(\overline{HU_{l}^{pt}}\) ), averaged over a set of 10 real patient CT or CBCT images, respectively; \(HU_{original\ OAR_{i}}^{phantom}\) is the HU value in the original HN phantom CT or CBCT image for each voxel. The \(\overline{HU_{OAR}^{pt}}\) and \(\overline{HU_{l}^{pt}}\) parameters were measured by taking the mean value of several (typically four) square ROIs of 10 \(\times\) 10 pixels, placed in different positions inside the OAR and its surrounding tissue, to reflect the typical HU variation in real patient images. These mean values were then averaged over a set of 10 pairs of actual patient CT and CBCT images. This process was done separately for the CT and CBCT image sets (i.e., for each patient, one CT and one CBCT image were considered). Using this method, the intrinsic noise of each image type, the CT and CBCT, was maintained in the digitally modified phantom image sets. The post-processed phantom images are in the standard DICOM [15] format, in order to be compatible with different image registration systems.