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The introduction of the MR-linac marked an important milestone for radiotherapy. The MR-linac uses a linear accelerator (Linac) combined with an MRI scanner. MRI allows excellent soft-tissue visualization, and the integrated system enables 3D imaging immediately before and during treatment. Finally, we can distinguish tumor from the healthy tissues while the patient is on the table and thus irradiate the tumor much more precisely and safely. This makes it possible to adjust treatment plans daily or even during radiation according to the patient's anatomy, also known as online adaptive radiotherapy.

It is essential to perform quality assurance (QA) for such adaptive treatments. In this thesis, the online adaptive workflow of the MR-linac was extensively validated. We investigated system-related uncertainties in dose calculations, the reliability of the adaptive workflow and the development of phantoms and detectors to provide QA for treatments in which the tumor moves and changes shape.

First it was shown that dosimetric uncertainties are small and consistent, regardless of tumor location. Next, the uncertainty of multiple adaptive fractions was shown to be similar to standard single fraction workflows. Spatial uncertainty in accumulated dose of these adaptive workflows could be quantified with our developed δ index. Furthermore, intra-fraction adaptations during prostate treatments improved dosimetric outcomes compared to treatments without adaptations. Finally, we validated the initial performance of a novel MRI-compatible prototype of a deformable motion phantom with integrated plastic scintillation dosimeters. In addition,  we showed that these dosimeters can be efficiently calibrated with a novel, faster method without loss of accuracy. 

The workflows and adaptation methods on the MR-linac will continue to evolve. As these innovations become standard practice, it is essential for QA protocols to evolve in parallel, meeting the demands of modern radiotherapy.