Learning nonrigid deformations for constrained multi-modal image registration

Med Image Comput Comput Assist Interv. 2013;16(Pt 3):171-8. doi: 10.1007/978-3-642-40760-4_22.

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.

MeSH terms

  • Algorithms
  • Artificial Intelligence*
  • Epilepsy / diagnosis*
  • Humans
  • Image Enhancement / methods
  • Image Interpretation, Computer-Assisted / methods*
  • Magnetic Resonance Imaging / methods*
  • Pattern Recognition, Automated / methods*
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Subtraction Technique*
  • Tomography, X-Ray Computed / methods*