## 1 Introduction  
External beam radiotherapy is gradually evolving towards real-time adaptive radiotherapy (ART) [1], which itself is being developed as a new paradigm in radiation oncology [2]. However, ART is, at present, not standardised or widely employed [3]. This is due, not only to the time-consuming processes of the delineation of the targets and organs at risk on the daily computed tomography (CT) images and treatment re-planning, but also to the limited quality of the daily on-board cone beam computed tomography (CBCT) images. Such limitations add practical constraints to the ability to delineate structures manually and estimate the daily delivered dose accurately, and they also contribute to the limited accuracy of the registration between the daily CBCT and planning CT images [4]. The development of automated tools that enable high-quality ART is likely to lead to significant efficiency gains and foster wider clinical uptake [5]. In the process of delivering automated ART solutions, a challenging task is the validation of the image registration algorithms for clinical use, as their performance depends on the complexity and quality of the images used in the registration task [6]. In the ART workflow, the deformable vector field (DVF), which is generated during the deformable image registration (DIR), could also be applied for dose mapping and dose accumulation purposes. In this process, any uncertainty in the DVF generation would be propagated directly to the calculated dose map. Considerable research has been done in the investigation of DIR accuracy, primarily through the creation of evaluation metrics. Multi-institutional studies have been performed to validate the use of DIR algorithms [7; 8] and these found variations in the DIR performance among different institutions as a result of differences in the procedure, therefore, the accuracy of the DIR was found to be site-specific. A recent survey by [PERSON] et al. [9] highlighted that the validation of the DIR registration is currently an urgent problem to solve. The report of the American Association of Physicists in Medicine (AAPM) Task Group 132 (TG132) [10] provided an essential set of guidelines for the quality assurance (QA) and quality control of the image registration operations for the overall clinical process. The TG132 report recommends a series of tests and corresponding metrics that should be evaluated and reported during the image registration software verification. The TG132 suggests the use of a known DVF to test deformable image registration [10]; this method is generally used with digital phantoms that consist of two image sets linked via a known DVF (ground truth). Several phantom data sets have been generated by the TG132 [10] for use in QA programmes for this purpose. However, a few groups found an incompatibility of certain digital phantoms, provided by the TG132 report, with commercial software [11; 12], and/or they found some of the provided phantom images were still limited in their utility. For example, only a pelvic anatomical phantom was made publicly available for DIR verification, and only a single known transformation (refDVF) was provided for test purposes [10]. [PERSON] et al. [13] reported the differences observed between 10 virtual head and neck (HN) phantoms for DIR verification over five different commercial software systems; these results emphasized the need to assess the DIR accuracy on each considered clinical DIR. A recent (2021) UK survey [14] highlighted that there is a need for additional guidelines, training and better tools for commissioning DIR software and the evaluation of registration results, which should include the development of recommendations on which quantitative metrics to use.