Abstract
An important research problem in image-guided radiation therapy is how to accurately register daily onboard Cone-beam CT (CBCT) images to higher quality pretreatment fan-beam CT (FBCT) images. Assuming the organ segmentations are both available on CBCT and FBCT images, methods have been proposed to use them to help the intensity-driven image registration. Due to the low contrast between soft-tissue structures exhibited in CBCT, the interobserver contouring variability (expressed as standard deviation) can be as large as 2-3 mm and varies systematically with organ, and relative location on each organ surface. Therefore the inclusion of the segmentations into registration may degrade registration accuracy. To address this issue we propose a surface assimilation method that estimates a new surface from the manual segmentation from a priori organ shape knowledge and the interobserver segmentation error. Our experiment results show the proposed method improves registration accuracy compared to previous methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Balik, S., Weiss, E., Jan, N., Roman, N., Sleeman, W.C., Fatyga, M., Christensen, G.E., Zhang, C., Murphy, M.J., Lu, J., Keall, P., Williamson, J.F., Hugo, G.D.: Evaluation of 4- dimensional computed tomography to 4-dimensional cone-beam computed tomography deformable image registration for lung cancer adaptive radiation therapy. International Journal of Radiation Oncology*Biology*Physics 86(2), 372–379 (2013)
Christensen, G., Carlson, B., Chao, K., Yin, P., Grigsby, P., Nguyen, K., Dempsey, J., Lerma, F., Bae, K., Vannier, M., et al.: Image-based dose planning of intracavitary brachytherapy: registration of serial-imaging studies using deformable anatomic templates. International Journal of Radiation Oncology Biology Physics 51(1), 227–243 (2001)
Cootes, T., Taylor, C., Cooper, D., Graham, J., et al.: Active shape models-their training and application. Computer Vision and Image Understanding 61(1), 38–59 (1995)
Craven, P., Wahba, G.: Smoothing noisy data with spline functions. Numerische Mathematik 31(4), 377–403 (1978)
Davies, R.H., Twining, C.J., Taylor, C.J.: Statistical models of shape - optimisation and evaluation. Springer (2008)
Dawson, L.A., Sharpe, M.B.: Image-guided radiotherapy: rationale, benefits, and limitations. The Lancet Oncology 7(10), 848–858 (2006)
Fitzpatrick, J., West, J., Maurer Jr., C.R.: Predicting error in rigid-body point-based registration. IEEE Transactions on Medical Imaging 17(5), 694–702 (1998)
Greene, W., Chelikani, S., Purushothaman, K., Knisely, J., Chen, Z., Papademetris, X., Staib, L., Duncan, J.: Constrained non-rigid registration for use in image-guided adaptive radiotherapy. Medical Image Analysis 13(5), 809–817 (2009)
Kalnay, E.: Atmospheric modeling, data assimilation, and predictability. Cambridge University Press (2003)
Lu, C., Chelikani, S., Papademetris, X., Knisely, J.P., Milosevic, M.F., Chen, Z., Jaffray, D.A., Staib, L.H., Duncan, J.S.: An integrated approach to segmentation and nonrigid registration for application in image-guided pelvic radiotherapy. Medical Image Analysis 15(5), 772–785 (2011)
Remeijer, P., Rasch, C., Lebesque, J.V., van Herk, M.: A general methodology for three-dimensional analysis of variation in target volume delineation. Medical Physics 26(6), 931–940 (1999)
Risholm, P., Janoos, F., Norton, I., Golby, A., Wells III, W.: Bayesian characterization of uncertainty in intra-subject non-rigid registration. Med. Image Anal. 17(5), 538–555 (2013)
Rueckert, D., Sonoda, L., Hayes, C., Hill, D., Leach, M., Hawkes, D.: Nonrigid registration using free-form deformations: application to breast mr images. IEEE Transactions on Medical Imaging 18(8), 712–721 (1999)
Wahba, G.: Spline models for observational data, vol. 59. Society for Industrial Mathematics (1990)
Wang, Y.: Smoothing splines: methods and applications. Taylor & Francis US (2011)
Wu, J., Murphy, M.J., Weiss, E., Sleeman IV, W.C., Williamson, J.: Development of a population-based model of surface segmentation uncertainties for uncertainty-weighted deformable image registrations. Medical Physics 37(2), 607–614 (2010)
Zhang, C., Christensen, G.E., Kurtek, S., Srivastava, A., Murphy, M.J., Weiss, E., Bai, E., Williamson, J.F.: SUPIR: Surface uncertainty-penalized, non-rigid image registration for pelvic CT imaging. In: Dawant, B.M., Christensen, G.E., Fitzpatrick, J.M., Rueckert, D. (eds.) WBIR 2012. LNCS, vol. 7359, pp. 236–245. Springer, Heidelberg (2012)
Zhou, J., Kim, S., Jabbour, S., Goyal, S., Haffty, B., Chen, T., Levinson, L., Metaxas, D., Yue, N.J.: A 3d global-to-local deformable mesh model based registration and anatomy-constrained segmentation method for image guided prostate radiotherapy. Medical Physics 37(3), 1298–1308 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhang, C., Christensen, G.E., Murphy, M.J., Weiss, E., Williamson, J.F. (2014). Non-rigid Image Registration with Equally Weighted Assimilated Surface Constraint. In: Ourselin, S., Modat, M. (eds) Biomedical Image Registration. WBIR 2014. Lecture Notes in Computer Science, vol 8545. Springer, Cham. https://doi.org/10.1007/978-3-319-08554-8_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-08554-8_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08553-1
Online ISBN: 978-3-319-08554-8
eBook Packages: Computer ScienceComputer Science (R0)