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Validation and Comparison of Approaches to Respiratory Motion Estimation

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Book cover 4D Modeling and Estimation of Respiratory Motion for Radiation Therapy

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

Abstract

The accuracy of respiratory motion estimation has a direct impact on the success of clinical applications such as diagnosis, as well as planning, delivery, and assessment of therapy for lung or other thoracic diseases. While rigid registration is well suited to validation and has reached a mature state in clinical applications, for non-rigid registration no gold-standard exists. This chapter investigates the validation of non-rigid registration accuracy with a focus on lung motion. The central questions addressed in this chapter are (1) how to measure registration accuracy, (2) how to generate ground-truth for validation, and (3) how to interpret accuracy assessment results.

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Notes

  1. 1.

    http://www.grand-challenge.org/index.php/MICCAI_2010_Workshop

  2. 2.

    http://www.dir-lab.com

  3. 3.

    http://www.creatis.insa-lyon.fr/rio/popi-model

  4. 4.

    http://vessel12.grand-challenge.org

  5. 5.

    http://image.diku.dk/exact

  6. 6.

    http://www.lola11.com

  7. 7.

    Symmetric registration approaches are independent of the order of input images while asymmetric approaches are not. Naturally, a validation metric based on a specific criterion is not designed for approaches already relying on the same criterion.

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This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

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Authors and Affiliations

  1. Philips Research Laboratories, Hamburg, Germany

    Sven Kabus & Tobias Klinder

  2. Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands

    Keelin Murphy

  3. University Medical Center Hamburg-Eppendorf, Hamburg, Germany

    René Werner

  4. Université de Lyon, CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, Université Lyon 1, Centre Léon Bérard, Lyon, France

    David Sarrut

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  1. Sven Kabus
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Correspondence to Sven Kabus .

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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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Kabus, S., Klinder, T., Murphy, K., Werner, R., Sarrut, D. (2013). Validation and Comparison of Approaches to Respiratory Motion Estimation. 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_8

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