Abstract
Breathing motion complicates many image-guided interventions working on the thorax or upper abdomen. However, prior knowledge provided by a statistical breathing model, can reduce the uncertainties of organ location. In this paper, a prediction framework for statistical motion modeling is presented and different representations of the dynamic data for motion model building of the lungs are investigated. Evaluation carried out on 4D-CT data sets of 10 patients showed that a displacement vector-based representation can reduce most of the respiratory motion with a prediction error of about 2 mm, when assuming the diaphragm motion to be known.
Chapter PDF
Similar content being viewed by others
Keywords
- Motion Model
- Principal Component Regression
- Motion Prediction
- Breathing Motion
- Deformable Image Registration
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Keall, P., Mageras, G., Balter, J., et al.: The management of resp. motion in radiation oncology report of AAPM Task group. Med. Phys. 33 (10), 3874–3900 (2006)
Bosch, J., Mitchell, S., Lelieveldt, B., et al.: Automatic segmentation of echocardiographic sequences by active appearance motion models. IEEE Transactions on Medical Imaging 21 (11), 1374–1383 (2002)
Casero, R., Noble, J.: A novel explicit 2D+t cyclic shape model applied to echocardiography. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 527–534. Springer, Heidelberg (2008)
Ehrhardt, J., Werner, R., Schmidt-Richberg, A., et al.: Generation of a mean motion model of the lung using 4D-CT image data. In: Eurographics Workshop on Visual Computing for Biomedicine (2008)
McClelland, J., Blackall, J., Tarte, S., et al.: A continuous 4D motion model from multiple respiratory cycles for use in lung radiotherapy. Med. Phys. 33 (9), 3348–3358 (2006)
Zhang, Q., Pevsner, A., Hertanto, A., et al.: A patient-specific respiratory model of anatomical motion for radiation treatment planning. Medical Physics 34(12), 4772–4782 (2007)
von Berg, J., Barschdorf, H., Blaffert, T., et al.: Surface based cardiac and respiratory motion extraction motion extraction for pulmonary structures from multi-phase CT. In: Proc. of SPIE, vol. 6511, pp. 65110Y1–11 (2007)
Castillo, R., Castillo, E., Guerra, R., et al.: A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets. Physics in Medicine and Biology 54, 1849–1870 (2009)
Rosipal, R., Krämer, N.: Overview and recent advances in partial least squares. In: Saunders, C., Grobelnik, M., Gunn, S., Shawe-Taylor, J. (eds.) SLSFS 2005. LNCS, vol. 3940, pp. 34–51. Springer, Heidelberg (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Klinder, T., Lorenz, C., Ostermann, J. (2010). Prediction Framework for Statistical Respiratory Motion Modeling. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010. MICCAI 2010. Lecture Notes in Computer Science, vol 6363. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15711-0_41
Download citation
DOI: https://doi.org/10.1007/978-3-642-15711-0_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15710-3
Online ISBN: 978-3-642-15711-0
eBook Packages: Computer ScienceComputer Science (R0)