Surrogate-Driven Estimation of Respiratory Motion and Layers in X-Ray Fluoroscopy

Dense motion estimation in X-ray fluoroscopy is challenging due to low soft-tissue contrast and the transparent projection of 3-D information to 2-D. Motion layers have been introduced as an intermediate representation, but so far failed to generate plausible motions because their estimation is ill-posed. To attain plausible motions, we include prior information for each motion layer in the form of a surrogate signal. In particular, we extract a respiratory signal from the images using manifold learning and use it to define a surrogate-driven motion model. The model is incorporated into an energy minimization framework with smoothness priors to enable motion estimation. Experimentally, our method estimates 48% of the 2-D motion field on XCAT phantom data. On real X-ray sequences, the target registration error of manually annotated landmarks is reduced by 52%. In addition, we qualitatively show that a meaningful separation into motion layers is achieved.