To optimize treatment in patients with craniosynostosis, better understanding of the disease process is essential, for example using brain MRI analysis to study brain volume, brain perfusion, and brain micro-architecture. However, such analyses require image registration, which is challenging because of disease-related brain deformations. Therefore, the aim of this project is to optimize image registration for children with syndromic craniosynostosis, aged 0 to 6 years old. We compared conventional and deep learning registration methods in a quantitative evaluation using synthetic data (i.e. deformed atlases) and in a qualitative experiment using registration of atlas scans to craniosynostosis scans. In addition to comparing registration methods, we evaluate the in uence of using both T1-weighted and T2-weighted scans and using an infant or adult atlas. Our qualitative results showed that head shape was registered well by both the conventional and the deep learning registration method, while the deep learning method performed better regarding registration of the ventricles. Quantitatively, our results showed that white matter structures were registered well (Dice: 0.70-0.81). However, regarding registration of the cortical brain regions, both methods resulted in a sub-optimal accuracy (Dice: 0.45-0.63). In general, the approaches of using T2-weighted infant atlases or T1-weighted adult atlases outperformed the alternative approaches. In conclusion, we obtained the best registration result using the deep learning approach, probably as prior spatial information is incorporated in the training process. In addition, we showed that infant atlases based on T2-weighted scans lead to the best results in registration of infant scans.