Abstract
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.
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Fischer, P., Pohl, T., Maier, A., Hornegger, J. (2015). Surrogate-Driven Estimation of Respiratory Motion and Layers in X-Ray Fluoroscopy. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science(), vol 9349. Springer, Cham. https://doi.org/10.1007/978-3-319-24553-9_35
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DOI: https://doi.org/10.1007/978-3-319-24553-9_35
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