Efficient framework for deformable 2D-3D registration

Using 2D-3D registration it is possible to extract the body transformation between the coordinate systems of X-ray and volumetric CT images. Our initial motivation is the improvement of accuracy of external beam radiation therapy, an effective method for treating cancer, where CT data play a central role in radiation treatment planning. Rigid body transformation is used to compute the correct patient setup. The drawback of such approaches is that the rigidity assumption on the imaged object is not valid for most of the patient cases, mainly due to respiratory motion. In the present work, we address this limitation by proposing a flexible framework for deformable 2D-3D registration consisting of a learning phase incorporating 4D CT data sets and hardware accelerated free form DRR generation, 2D motion computation, and 2D-3D back projection.