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Computational Motion Phantoms and Statistical Models of Respiratory Motion

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4D Modeling and Estimation of Respiratory Motion for Radiation Therapy

Part of the book series: Biological and Medical Physics, Biomedical Engineering ((BIOMEDICAL))

Abstract

Breathing motion is not a robust and 100 % reproducible process, and inter- and intra-fractional motion variations form an important problem in radiotherapy of the thorax and upper abdomen. A widespread consensus nowadays exists that it would be useful to use prior knowledge about respiratory organ motion and its variability to improve radiotherapy planning and treatment delivery. This chapter discusses two different approaches to model the variability of respiratory motion. In the first part, we review computational motion phantoms, i.e. computerized anatomical and physiological models. Computational phantoms are excellent tools to simulate and investigate the effects of organ motion in radiation therapy and to gain insight into methods for motion management. The second part of this chapter discusses statistical modeling techniques to describe the breathing motion and its variability in a population of 4D images. Population-based models can be generated from repeatedly acquired 4D images of the same patient (intra-patient models) and from 4D images of different patients (inter-patient models). The generation of those models is explained and possible applications of those models for motion prediction in radiotherapy are exemplified. Computational models of respiratory motion and motion variability have numerous applications in radiation therapy, e.g. to understand motion effects in simulation studies, to develop and evaluate treatment strategies or to introduce prior knowledge into the patient-specific treatment planning.

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Notes

  1. 1.

    http://dmip1.rad.jhmi.edu/xcat/

  2. 2.

    Note that in general iterative optimization methods are used to compute the static velocity field of the composition \(T_{p\rightarrow ref}=\varPsi _{p\rightarrow ref}\circ T_{p}\circ \varPsi _{p\rightarrow ref}^{-1}\).

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  1. University of Lübeck, Lübeck, Germany

    Jan Ehrhardt

  2. Philips Research Laboratories, Hamburg, Germany

    Tobias Klinder & Cristian Lorenz

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  1. Jan Ehrhardt
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  2. Tobias Klinder
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  3. Cristian Lorenz
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Correspondence to Jan Ehrhardt .

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  1. , Institut für Medizinische Informatik, Universität Lübeck, Ratzeburger Allee 160, Lübeck, 23538, Germany

    Jan Ehrhardt

  2. Forschungslaboratorien, Digital Imaging 24, Philips Technologie GmbH, Röntgenstraße 24 - 26, Hamburg, 22335, Germany

    Cristian Lorenz

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Ehrhardt, J., Klinder, T., Lorenz, C. (2013). Computational Motion Phantoms and Statistical Models of Respiratory Motion. In: Ehrhardt, J., Lorenz, C. (eds) 4D Modeling and Estimation of Respiratory Motion for Radiation Therapy. Biological and Medical Physics, Biomedical Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36441-9_10

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  • DOI: https://doi.org/10.1007/978-3-642-36441-9_10

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