title: Learning nonrigid deformations for constrained multi-modal image registration.
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.
authors: Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA. john.onofrey@yale.edu
authors: John A
authors: JA
authors: Onofrey
authors: Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA.
authors: Lawrence H
authors: LH
authors: Staib
authors: Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA.
authors: Xenophon
authors: X
authors: Papademetris
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D004827:Epilepsy / Q000175:diagnosis*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*