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Estimation of Lung Ventilation

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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))

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Abstract

Since the primary function of the lung is gas exchange, ventilation can be interpreted as an index of lung function in addition to perfusion. Injury and disease processes can alter lung function on a global and/or a local level. MDCT can be used to acquire multiple static breath-hold CT images of the lung taken at different lung volumes, or with proper respiratory control, 4DCT images of the lung reconstructed at different respiratory phases. Image registration can be applied to this data to estimate a deformation field that transforms the lung from one volume configuration to the other. This deformation field can be analyzed to estimate local lung tissue expansion, calculate voxel-by-voxel intensity change, and make biomechanical measurements. The physiologic significance of the registration-based measures of respiratory function can be established by comparing to more conventional measurements, such as nuclear medicine or contrast wash-in/wash-out studies with CT or MR. An important emerging application of these methods is the detection of pulmonary function change in subjects undergoing radiation therapy (RT) for lung cancer. During RT, treatment is commonly limited to sub-therapeutic doses due to unintended toxicity to normal lung tissue. Measurement of pulmonary function may be useful as a planning tool during RT planning, may be useful for tracking the progression of toxicity to nearby normal tissue during RT, and can be used to evaluate the effectiveness of a treatment post-therapy. This chapter reviews the basic measures to estimate regional ventilation from image registration of CT images, the comparison of them to the existing golden standard and the application in radiation therapy.

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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. The University of Iowa, Iowa, USA

    Kai Ding, Kaifang Du, Ryan Amelon, Gary E. Christensen, Madhavan Raghavan & Joseph M. Reinhardt

  2. Biomedical Image Analysis Lab, GE Global Research Center, Niskayuna, USA

    Kunlin Cao

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  1. Kai Ding
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  2. Kunlin Cao
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  3. Kaifang Du
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  4. Ryan Amelon
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  5. Gary E. Christensen
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  6. Madhavan Raghavan
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  7. Joseph M. Reinhardt
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Correspondence to Joseph M. Reinhardt .

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Editors and Affiliations

  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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Ding, K. et al. (2013). Estimation of Lung Ventilation. 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_13

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  • DOI: https://doi.org/10.1007/978-3-642-36441-9_13

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  • Print ISBN: 978-3-642-36440-2

  • Online ISBN: 978-3-642-36441-9

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