International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2015: Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015 pp 282-289 | Cite as

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

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

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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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Peter Fischer
    • 1
  • 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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