## 4 Discussion
### Similar DIR Accuracy Workflows to the Current Study  
Our proposed method quantified the effect of the DIR on the dose distribution, focusing on the DVF accuracy. As was reported in the introduction, for adaptive radiation therapy, the accurate dose deformation and summation/accumulation within each contour is determined by the DVF accuracy [16]. Other methods evaluated the dose deformation accuracy, focusing on the DVF. Recently, [PERSON] et al. [35] assessed the uncertainty in the dose accumulation procedure for the HN adaptive by comparing the DIR-facilitated dose accumulation using a commercial software system with the results of an in-silico model, based on the clinically observed deformations as the ground truth. HN patients CT and CBCTs images were used to generate in silico reference CBCTs and DVFs, as the ground truth, with a second reference B-spline DIR software. The in silico images, generated with an independent DIR software as the reference, were necessary to have a known reference DVF. Also, this method has the advantage of evaluating each patient-specific deformation and dose warping. However, [PERSON]'s method works directly on the patients' images, making the clinical workflow simpler and reasonably quicker than the proposed one, avoiding the use of the HN phantom presented in this study, with its related inherent limitations (see Section 4.1). The complexity of the deformations in the human body, as well as its heterogeneity in terms of the HU values, are challenging to model accurately through a phantom. On the other hand, the method implemented in our work has the advantage of replicating the HU distribution found in the OARs on clinical images. In comparison, [PERSON]'s method tested the DIRbetween the planning CT and the in silico model of the CBCT, which is different from the original CBCT in the HU and quality (the planning CT deformed to the anatomy of the CBCT of the considered fraction) and could not consider some issues of the CTBT-to-CT DIR, including the reduction in the accuracy due to the low image contrast of the real CBCT. [PERSON] et al. [13] evaluated the performance of several DIR software systems using several HN digital phantoms, in terms of both the spatial and dosimetric accuracy at the voxel level. The study created publicly available phantoms based on the CT data from head and neck cancer patients. These phantoms provide a clinically based ground-truth model that encompasses the anatomical changes that occur over the course of a typical treatment. Also, this study assessed the deformation accuracy for each voxel by comparing the entire registration DVF to the ground-truth DVF, based on the clinical scenario. One key aspect of our study was the possibility to use real patient data (the DVF and dose distributions) to evaluate each patient-specific DIR, instead of commissioning specific DIR algorithms. Moreover, the study of [PERSON] was focused on CT-to-CT registrations, while our study referred to multimodal CBCT-to-CT registration. The latter presents more issues in the DIR accuracy, due to the limited quality of the CBCT images. [PERSON] et al. [17] investigated the transformations that mapped the anatomy between the planning CT and CBCT using four different DIR approaches. DVFs were used to remap the dose of the day onto the planning CT. The data from five HN patients were used to evaluate the performance of each implementation, based on geometrical matching, the physical properties of the DVFs, and the similarity between the warped dose distributions. As reported in the Introduction, this approach can be useful to choose the optimal registration method, without conveying any information on the DIR uncertainties and related dose errors. [PERSON] et al. [36] used a deformable HN phantom to test DIR algorithms, but, in practice, it is difficult to produce physical phantoms for every deformation scenario that occurs clinically.