 |
    |
Learning Nonrigid Deformations for Constrained Multi-modal Image Registration
John A. Onofrey1, Lawrence H. Staib1, 2, 3, and Xenophon Papademetris1, 3
1Departments of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
john.onofrey@yale.edu
lawrence.staib@yale.edu
xenophon.papademetris@yale.edu
2Electrical Engineering, Yale University, New Haven, CT 06520, USA
3Diagnostic Radiology, Yale University, New Haven, CT 06520, USA
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.
Keywords: nonrigid registration, multi-modal, statistical deformation model, principal component analysis, image-guided surgery
LNCS 8151, p. 171 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2013