Simulation of Range Imaging-based Estimation of Respiratory Lung Motion

M. Wilms; R. Werner; M. Blendowski; J. Ortmüller; H. Handels

doi:10.3414/ME13-01-0137

Methods of Information in Medicine, Table of Contents

Methods Inf Med 2014; 53(04): 257-263
DOI: 10.3414/ME13-01-0137

Original Articles

Schattauer GmbH

Simulation of Range Imaging-based Estimation of Respiratory Lung Motion

Influence of Noise, Signal Dimensionality and Sampling Patterns

Authors

M. Wilms

¹Institute of Medical Informatics, University of Lübeck, Lübeck, Germany
R. Werner

²Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
M. Blendowski

¹Institute of Medical Informatics, University of Lübeck, Lübeck, Germany
J. Ortmüller

¹Institute of Medical Informatics, University of Lübeck, Lübeck, Germany
H. Handels

¹Institute of Medical Informatics, University of Lübeck, Lübeck, Germany

Abstract

Summary

Objectives: A major problem associated with the irradiation of thoracic and abdominal tumors is respiratory motion. In clinical practice, motion compensation approaches are frequently steered by low-dimensional breathing signals (e.g., spirometry) and patient-specific correspondence models, which are used to estimate the sought internal motion given a signal measurement. Recently, the use of multidimensional signals derived from range images of the moving skin surface has been proposed to better account for complex motion patterns. In this work, a simulation study is carried out to investigate the motion estimation accuracy of such multidimensional signals and the influence of noise, the signal dimensionality, and different sampling patterns (points, lines, regions).

Methods: A diffeomorphic correspondence modeling framework is employed to relate multidimensional breathing signals derived from simulated range images to internal motion patterns represented by diffeomorphic non-linear transformations. Furthermore, an automatic approach for the selection of optimal signal combinations/patterns within this framework is presented.

Results: This simulation study focuses on lung motion estimation and is based on 28 4D CT data sets. The results show that the use of multidimensional signals instead of one-dimensional signals significantly improves the motion estimation accuracy, which is, however, highly affected by noise. Only small differences exist between different multidimensional sampling patterns (lines and regions). Automatically determined optimal combinations of points and lines do not lead to accuracy improvements compared to results obtained by using all points or lines.

Conclusions: Our results show the potential of multidimensional breathing signals derived from range images for the model-based estimation of respiratory motion in radiation therapy.

Keywords

Respiratory motion - motion estimation - correspondence modeling - regression - image registration

Full Text

References

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