Nonrigid 3d brain registration using intensity/feature information

Authors:
Christine DeLorenzo;Xenophon Papademetris;Kun Wu;Kenneth P. Vives;Dennis Spencer;James S. Duncan
Affiliations:
Departments of Electrical Engineering, Yale University, New Haven, CT;Departments of Electrical Engineering, Yale University, New Haven, CT;Departments of Neurosurgery, Yale University, New Haven, CT;Departments of Neurosurgery, Yale University, New Haven, CT;Departments of Neurosurgery, Yale University, New Haven, CT;Departments of Electrical Engineering, Yale University, New Haven, CT
Venue:
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Year:
2006

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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.