Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Clinical Trial
. 2006;9(Pt 1):932-9.
doi: 10.1007/11866565_114.

Nonrigid 3D brain registration using intensity/feature information

Christine DeLorenzo  1 , Xenophon Papademetris, Kun Wu, Kenneth P Vives, Dennis Spencer, James S Duncan
Affiliations
Clinical Trial

Nonrigid 3D brain registration using intensity/feature information

Christine DeLorenzo et al. Med Image Comput Comput Assist Interv. 2006.

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.

PubMed Disclaimer

Figures

Fig. 1
Fig. 1
The left image depicts the extracted 3D brain surface. The craniotomy was performed in the highlighted section. The right image shows initial (dark spheres) and final (light spheres) brain surface positions as acquired in the OR for algorithm validation. The hardware and software required for point acquisition are explained in [8].
Fig. 2
Fig. 2
Stereo camera intraoperative images acquired approximately three hours after surgery began. The sulci used as the feature information are outlined.
Fig. 3
Fig. 3
The color of the surfaces patches was found by backprojecting the image intensities to the surface using the camera calibration matrices. The cortical surface sinks during surgery from its initial position, out of the intraoperative camera view, to the deformed position. The surface tracking algorithm takes advantage of both the image intensities and the feature information to closely determine the final cortical position.
See this image and copyright information in PMC

References

    1. Audette MA, Siddiqi K, Ferrie FP, Peters T. An integrated range-sensing, segmentation and registration framework for the characterization of intra-surgical brain deformations in image-guided surgery. Computer Vision and Image Understanding. 2003;89:226–251.
    1. DeLorenzo C, Papademetris X, Vives KP, Spencer D, Duncan JS. International Symposium on Biomedical Imaging. ISBI; 2006. Combined feature/intensity-based brain shift compensation using stereo guidance; pp. 335–338.
    1. Ferrant M, Nabavi A, Macq B, Black PM, Jolesz FA. Serial registration of intraoperative MR images of the brain. Medical Image Analysis. 2002;6(4):337–360. - PubMed
    1. Gering DT, Nabavi A, Kikinis R, Grimson WEL, Hata N, Everett P, Jolesz F, Wells WM. Medical Image Computing and Computer-Assisted Intervention. MICCAI; 1999. An integrated visualization system for surgical planning and guidance using image fusion and intervenattional imaging; pp. 809–819.
    1. Kopparapu SK, Corke P. The effect of measurement noise on intrinsic camera calibration parameters. Proceedings of the 1999 IEEE International Conference on Robot and Automation; May 1999; pp. 1281–1286.

Publication types

LinkOut - more resources