  Affine registration of one or several brain image(s) onto a common reference
space is a necessary prerequisite for many image processing tasks, such as
brain segmentation or functional analysis. Manual assessment of registration
quality is a tedious and time-consuming task, especially in studies comprising
a large amount of data. An automated and reliable quality control (QC) becomes
mandatory. Moreover, the computation time of the QC must be also compatible
with the processing of massive datasets. Therefore, an automated deep neural
network approaches appear as a method of choice to automatically assess
registration quality.
  In the current study, a compact 3D convolutional neural network (CNN),
referred to as RegQCNET, is introduced to quantitatively predict the amplitude
of an affine registration mismatch between a registered image and a reference
template. This quantitative estimation of registration error is expressed using
metric unit system. Therefore, a meaningful task-specific threshold can be
manually or automatically defined in order to distinguish usable and non-usable
images.
  The robustness of the proposed RegQCNET is first analyzed on lifespan brain
images undergoing various simulated spatial transformations and intensity
variations between training and testing. Secondly, the potential of RegQCNET to
classify images as usable or non-usable is evaluated using both manual and
automatic thresholds. During our experiments, automatic thresholds are
estimated using several computer-assisted classification models through
cross-validation. To this end we used expert's visual quality control estimated
on a lifespan cohort of 3953 brains. Finally, the RegQCNET accuracy is compared
to usual image features.
  Results show that the proposed deep learning QC is robust, fast and accurate
to estimate affine registration error in processing pipeline.
