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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© 2017 Springer-Verlag GmbH Deutschland
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Geimer, T., Birlutiu, A., Unberath, M., Taubmann, O., Bert, C., Maier, A. (2017). A Kernel Ridge Regression Model for Respiratory Motion Estimation in Radiotherapy. In: Maier-Hein, geb. Fritzsche, K., Deserno, geb. Lehmann, T., Handels, H., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2017. Informatik aktuell. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-54345-0_38
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DOI: https://doi.org/10.1007/978-3-662-54345-0_38
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Publisher Name: Springer Vieweg, Berlin, Heidelberg
Print ISBN: 978-3-662-54344-3
Online ISBN: 978-3-662-54345-0
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