  Registration is a core component of many imaging pipelines. In case of
clinical scans, with lower resolution and sometimes substantial motion
artifacts, registration can produce poor results. Visual assessment of
registration quality in large clinical datasets is inefficient. In this work,
we propose to automatically assess the quality of registration to an atlas in
clinical FLAIR MRI scans of the brain. The method consists of automatically
segmenting the ventricles of a given scan using a neural network, and comparing
the segmentation to the atlas' ventricles propagated to image space. We used
the proposed method to improve clinical image registration to a general atlas
by computing multiple registrations and then selecting the registration that
yielded the highest ventricle overlap. Methods were evaluated in a single-site
dataset of more than 1000 scans, as well as a multi-center dataset comprising
142 clinical scans from 12 sites. The automated ventricle segmentation reached
a Dice coefficient with manual annotations of 0.89 in the single-site dataset,
and 0.83 in the multi-center dataset. Registration via age-specific atlases
could improve ventricle overlap compared to a direct registration to the
general atlas (Dice similarity coefficient increase up to 0.15). Experiments
also showed that selecting scans with the registration quality assessment
method could improve the quality of average maps of white matter hyperintensity
burden, instead of using all scans for the computation of the white matter
hyperintensity map. In this work, we demonstrated the utility of an automated
tool for assessing image registration quality in clinical scans. This image
quality assessment step could ultimately assist in the translation of automated
neuroimaging pipelines to the clinic.
