Abstract
We propose an effective 4D image registration algorithm for dynamic volumetric lung images. The registration will construct a deforming 3D model with continuous trajectory and smooth spatial deformation, and the model interpolates the interested region in the 4D (3D+T) CT images. The resultant non-rigid transformation is represented using two 4D B-spline functions, indicating a forward and an inverse 4D parameterization respectively. The registration process solves these two functions by minimizing an objective function that penalizes intensity matching error, feature alignment error, spatial and temporal non-smoothness, and inverse inconsistency. We test our algorithm for respiratory motion estimation on public benchmarks and on clinic lung CT data. The experimental results demonstrate the efficacy of our algorithm.
Chapter PDF
Similar content being viewed by others
References
Gorbunova, V., Sporring, J., Lo, P., Loeve, M., Tiddens, H., Nielsen, M., Dirksen, A., Bruijne, M.: Mass preserving image registration for lung CT. Medical Image Analysis 16(4), 786–795 (2012)
Metz, C., Klein, S., Schaap, M., van Walsum, T., Niessen, W.: Nonrigid registration of dynamic medical imaging data using nD + t B-splines and a groupwise optimization approach. Medical Image Analysis 15, 238–249 (2011)
Heinrich, M., Jenkinson, M., Brady, S., Schnabel, J.: Mrf-based deformable registration and ventilation estimation of lung CT. IEEE Trans. on Med. Imag. (2013)
Wu, G., Wang, Q., Lian, J., Shen, D.: Estimating the 4D respiratory lung motion by spatiotemporal registration and building super-resolution image. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part I. LNCS, vol. 6891, pp. 532–539. Springer, Heidelberg (2011)
Xue, Z., Wong, K., Wong, S.: Joint registration and segmentation of serial lung CT images for image-guided lung cancer diagnosis and therapy. Computerized Medical Imaging and Graphics 34(1), 55–60 (2010)
Wu, G., Wang, Q., Jia, H., Shen, D.: Feature-based groupwise registration by hierarchical anatomical correspondence detection. Human Brain Mapping 33(2), 253–271 (2012)
Xu, H., Li, X.: Consistent feature-aligned 4d image registration for respiratory motion modeling. In: Int. Symp. on Biom. Imaging, pp. 580–583 (2013)
Xu, H., Chen, P., Yu, W., Sawasnt, A., Iyengar, S., Li, X.: Feature-aligned 4D spatiotemporal image registration. In: Int. Conf. on Patt. Recog., pp. 2639–2642 (2012)
Christensen, G., Johnson, H.: Consistent image registration. IEEE Transaction on Medical Imaging 20(7), 568–582 (2001)
Vandemeulebroucke, J., Sarrut, D., Clarysse, P.: Point-validated pixel-based breathing thorax model. In: Int. Conf. on the Use of Comp. in Rad. Therapy (2007)
Castillo, R., Castillo, E., Guerra, R., Johnson, V.E., McPhail, T., Garg, A.K., Guerrero, T.: A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets. Phy. in Med. & Bio. 54, 1849–1870 (2009)
Iyengar, S., Li, X., Xu, H., Mukhopadhyay, S., Balakrishnan, N., Sawant, A., Iyengar, P.: Toward more precise radiotherapy treatment of lung tumors. IEEE Computer 45, 59–65 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Xu, H., Li, X. (2013). A Symmetric 4D Registration Algorithm for Respiratory Motion Modeling. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, vol 8150. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40763-5_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-40763-5_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40762-8
Online ISBN: 978-3-642-40763-5
eBook Packages: Computer ScienceComputer Science (R0)