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Nonrigid 3D Brain Registration Using Intensity/Feature Information

  • Christine DeLorenzo
  • Xenophon Papademetris
  • Kun Wu
  • Kenneth P. Vives
  • Dennis Spencer
  • James S. Duncan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4190)

Abstract

The brain deforms non-rigidly during neurosurgery, preventing preoperatively acquired images from accurately depicting the intraoperative brain. If the deformed brain surface can be detected, biomechanical models can be applied to calculate the resulting volumetric deformation. The reliability of this volumetric calculation is dependent on the accuracy of the surface detection. This work presents a surface tracking algorithm which relies on Bayesian analysis to track cortical surface movement. The inputs to the model are 3D preoperative brain images and intraoperative stereo camera images. The addition of a camera calibration optimization term creates a more robust model, capable of tracking the cortical surface in the presence of camera calibration error.

Keywords

Cortical Surface Camera Calibration Iterative Close Point Stereo Camera Brain Shift 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Christine DeLorenzo
    • 1
  • Xenophon Papademetris
    • 1
    • 2
  • Kun Wu
    • 3
  • Kenneth P. Vives
    • 3
  • Dennis Spencer
    • 3
  • James S. Duncan
    • 1
    • 2
  1. 1.Departments of Electrical EngineeringYale UniversityNew HavenUSA
  2. 2.Departments of Diagnostic RadiologyYale UniversityNew HavenUSA
  3. 3.Departments of NeurosurgeryYale UniversityNew HavenUSA

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