A Kernel Ridge Regression Model for Respiratory Motion Estimation in Radiotherapy

  • Tobias Geimer
  • Adriana Birlutiu
  • Mathias Unberath
  • Oliver Taubmann
  • Christoph Bert
  • Andreas Maier
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Abstract

This paper discusses a kernel ridge regression (KRR) model for motion estimation in radiotherapy. Using KRR, dense internal motion fields are estimated from high-dimensional surrogates without the need for prior dimensionality reduction. We compare the proposed model to a related approach with dimensionality reduction in the form of principal component analysis and principle component regression. Evaluation was performed in a simulation study based on nine 4D CT patient data sets achieving a mean estimation error of 0.84 ± 0.21mm for our approach.

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Copyright information

© Springer-Verlag GmbH Deutschland 2017

Authors and Affiliations

  • Tobias Geimer
    • 1
    • 2
  • Adriana Birlutiu
    • 3
  • Mathias Unberath
    • 1
    • 2
  • Oliver Taubmann
    • 1
    • 2
  • Christoph Bert
    • 2
    • 4
  • Andreas Maier
    • 1
    • 2
  1. 1.Friedrich-Alexander-Universität Erlangen-NürnbergPattern Recognition LabErlangen-NürnbergDeutschland
  2. 2.ErlangenGraduate School in Advanced Optical Technologies (SAOT)Erlangen-NürnbergDeutschland
  3. 3.”1 December 1918” University of Alba IuliaComputer Science DepartmentAlba IuliaRomania
  4. 4.Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-NürnbergDepartment of Radiation OncologyErlangenDeutschland

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