  We propose LiftReg, a 2D/3D deformable registration approach. LiftReg is a
deep registration framework which is trained using sets of digitally
reconstructed radiographs (DRR) and computed tomography (CT) image pairs. By
using simulated training data, LiftReg can use a high-quality CT-CT image
similarity measure, which helps the network to learn a high-quality deformation
space. To further improve registration quality and to address the inherent
depth ambiguities of very limited angle acquisitions, we propose to use
features extracted from the backprojected 2D images and a statistical
deformation model. We test our approach on the DirLab lung registration dataset
and show that it outperforms an existing learning-based pairwise registration
approach.
