Nonrigid 3D brain registration using intensity/feature information.
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.
DeLorenzo
Christine
C
Department of Electrical Engineering, Yale University, P.O. Box 208042 New Haven, CT 06520-8042, USA. christine.delorenzo@yale.edu
Papademetris
Xenophon
X
Wu
Kun
K
Vives
Kenneth P
KP
Spencer
Dennis
D
Duncan
James S
JS
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D001921:Brain / Q000473:pathology* / Q000601:surgery*; D004827:Epilepsy / Q000473:pathology / Q000601:surgery; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / 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; D025321:Surgery, Computer-Assisted / Q000379:methods*