  Image guidance in minimally invasive interventions is usually provided using
live 2D X-ray imaging. To enhance the information available during the
intervention, the preoperative volume can be overlaid over the 2D images using
2D/3D image registration. Recently, deep learning-based 2D/3D registration
methods have shown promising results by improving computational efficiency and
robustness. However, there is still a gap in terms of registration accuracy
compared to traditional optimization-based methods. We aim to address this gap
by incorporating traditional methods in deep neural networks using known
operator learning. As an initial step in this direction, we propose to learn
the update step of an iterative 2D/3D registration framework based on the
Point-to-Plane Correspondence model. We embed the Point-to-Plane Correspondence
model as a known operator in our deep neural network and learn the update step
for the iterative registration. We show an improvement of 1.8 times in terms of
registration accuracy for the update step prediction compared to learning
without the known operator.
