Abstract
We present a new strategy to constrain nonrigid registrations of multi-modal images using a low-dimensional statistical deformation model and test this in registering pre-operative and post-operative images from epilepsy patients. For those patients who may undergo surgical resection for treatment, the current gold-standard to identify regions of seizure involves craniotomy and implantation of intracranial electrodes. To guide surgical resection, surgeons utilize pre-op anatomical and functional MR images in conjunction with post-electrode implantation MR and CT images. The electrode positions from the CT image need to be registered to pre-op functional and structural MR images. The post-op MRI serves as an intermediate registration step between the pre-op MR and CT images. In this work, we propose to bypass the post-op MR image registration step and directly register the pre-op MR and post-op CT images using a low-dimensional nonrigid registration that captures the gross deformation after electrode implantation. We learn the nonrigid deformation characteristics from a principal component analysis of a set of training deformations and demonstrate results using clinical data. We show that our technique significantly outperforms both standard rigid and nonrigid intensity-based registration methods in terms of mean and maximum registration error.
Chapter PDF
Similar content being viewed by others
Keywords
References
Hartkens, T., Hill, D., Castellano-Smith, A., Hawkes, D., Maurer Jr., C.R., Martin, A., Hall, W., Liu, H., Truwit, C.: Measurement and analysis of brain deformation during neurosurgery. IEEE Trans. on Medical Imaging 22(1), 82–92 (2003)
He, T., Xue, Z., Xie, W., Wong, S.T.C.: Online 4-D CT estimation for patient-specific respiratory motion based on real-time breathing signals. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part III. LNCS, vol. 6363, pp. 392–399. Springer, Heidelberg (2010)
Joshi, A., Scheinost, D., Okuda, H., Belhachemi, D., Murphy, I., Staib, L., Papademetris, X.: Unified framework for development, deployment and robust testing of neuroimaging algorithms. Neuroinformatics 9, 69–84 (2011)
Kim, M.J., Kim, M.H., Shen, D.: Learning-based deformation estimation for fast non-rigid registration. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2008, pp. 1–6 (June 2008)
Papademetris, X., Jackowski, A.P., Schultz, R.T., Staib, L.H., Duncan, J.S.: Integrated intensity and point-feature nonrigid registration. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 763–770. Springer, Heidelberg (2004)
Rueckert, D., Frangi, A., Schnabel, J.: Automatic construction of 3-d statistical deformation models of the brain using nonrigid registration. IEEE Trans. on Medical Imaging 22(8), 1014–1025 (2003)
Rueckert, D., Sonoda, L., Hayes, C., Hill, D., Leach, M., Hawkes, D.: Nonrigid registration using free-form deformations: application to breast mr images. IEEE Trans. on Medical Imaging 18(8), 712–721 (1999)
Spencer, S.S., Sperling, M., Shewmon, A.: Intracranial electrodes. In: Epilepsy, A Comprehensive Textbook, pp. 1719–1748 (1998)
Studholme, C., Hill, D., Hawkes, D.: An overlap invariant entropy measure of 3d medical image alignment. Pattern Recognition 32(1), 71–86 (1999)
Xue, Z., Shen, D., Davatzikos, C.: Statistical representation of high-dimensional deformation fields with application to statistically constrained 3d warping. Medical Image Analysis 10(5), 740–751 (2006)
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
Onofrey, J.A., Staib, L.H., Papademetris, X. (2013). Learning Nonrigid Deformations for Constrained Multi-modal Image Registration. 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 8151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40760-4_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-40760-4_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40759-8
Online ISBN: 978-3-642-40760-4
eBook Packages: Computer ScienceComputer Science (R0)