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.