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

  • Peter FischerEmail author
  • Thomas Pohl
  • Andreas Maier
  • Joachim Hornegger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9349)


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.


Motion Estimation Respiratory Signal Surrogate Signal Target Registration Error Manifold Learning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Peter Fischer
    • 1
    Email author
  • Thomas Pohl
    • 2
  • Andreas Maier
    • 1
  • Joachim Hornegger
    • 1
  1. 1.Pattern Recognition Lab and Erlangen Graduate School in Advanced Optical Technologies (SAOT)FAU Erlangen-NürnbergErlangenGermany
  2. 2.Siemens HealthcareForchheimGermany

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