  Deep Learning-based 2D/3D registration methods are highly robust but often
lack the necessary registration accuracy for clinical application. A refinement
step using the classical optimization-based 2D/3D registration method applied
in combination with Deep Learning-based techniques can provide the required
accuracy. However, it also increases the runtime. In this work, we propose a
novel Deep Learning driven 2D/3D registration framework that can be used
end-to-end for iterative registration tasks without relying on any further
refinement step. We accomplish this by learning the update step of the 2D/3D
registration framework using Point-to-Plane Correspondences. The update step is
learned using iterative residual refinement-based optical flow estimation, in
combination with the Point-to-Plane correspondence solver embedded as a known
operator. Our proposed method achieves an average runtime of around 8s, a mean
re-projection distance error of 0.60 $\pm$ 0.40 mm with a success ratio of 97
percent and a capture range of 60 mm. The combination of high registration
accuracy, high robustness, and fast runtime makes our solution ideal for
clinical applications.
