  Alignment of contrast and non-contrast-enhanced imaging is essential for the
quantification of changes in several biomedical applications. In particular,
the extraction of cartilage shape from contrast-enhanced Computed Tomography
(CT) of tibiae requires accurate alignment of the bone, currently performed
manually. Existing deep learning-based methods for alignment require a common
template or are limited in rotation range. Therefore, we present a novel
network, D-net, to estimate arbitrary rotation and translation between 3D CT
scans that additionally does not require a prior standard template. D-net is an
extension to the branched Siamese encoder-decoder structure connected by new
mutual non-local links, which efficiently capture long-range connections of
similar features between two branches. The 3D supervised network is trained and
validated using preclinical CT scans of mouse tibiae with and without contrast
enhancement in cartilage. The presented results show a significant improvement
in the estimation of CT alignment, outperforming the current comparable
methods.
