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
. 2009 Sep 1;198(41):3313-3320.
doi: 10.1016/j.cma.2009.06.012.

Computation of intra-operative brain shift using dynamic relaxation

Grand Roman Joldes  1 , Adam Wittek, Karol Miller
Affiliations

Computation of intra-operative brain shift using dynamic relaxation

Grand Roman Joldes et al. Comput Methods Appl Mech Eng. .

Abstract

Many researchers have proposed the use of biomechanical models for high accuracy soft organ non-rigid image registration, but one main problem in using comprehensive models is the long computation time required to obtain the solution. In this paper we propose to use the Total Lagrangian formulation of the Finite Element method together with Dynamic Relaxation for computing intra-operative organ deformations. We study the best ways of estimating the parameters involved and we propose a termination criteria that can be used in order to obtain fast results with prescribed accuracy. The simulation results prove the accuracy and computational efficiency of the method, even in cases involving large deformations, nonlinear materials and contacts.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Influence of the minimum (a) and maximum (b) eigenvalue estimation on the convergence (only one eigenvalue is estimated, the other is assumed to be known). The modulus of the eigenvalues for matrix κ is represented against the real range of eigenvalues for matrix A.
Figure 2
Figure 2
Influence of eigenvalue estimations on the convergence (the estimation error for the minimum and maximum eigenvalues is shown in the legend). The modulus of the eigenvalues for matrix κ is represented against the real range of eigenvalues for matrix A.
Figure 3
Figure 3
Dominant eigenvalue estimation starting from an over-estimated (a) and an under-estimated (b) initial lower eigenvalue.
Figure 4
Figure 4
Absolute difference in nodal positions between our algorithm and Abaqus are colour coded. Dimensions are in meters.
Figure 5
Figure 5
Error estimation (in meters) for the ellipsoid deformation immediately after the loading was applied (a) and after 2000 steps (b).
Figure 6
Figure 6
The mesh used for the brain shift simulation (only the left half of the brain and skull meshes is shown).
Figure 7
Figure 7
Dominant eigenvalue estimation for the brain shift simulation.
Figure 8
Figure 8
Absolute nodal position error distribution after 1000 iterations (a) and after 2000 iterations (includes the image of the kull) (b). Dimensions are in meters.
Figure 9
Figure 9
Error estimation (in meters) for the brain shift simulation.
See this image and copyright information in PMC

References

    1. Bucholz R, MacNeil W, McDurmont L. The operating room of the future. Clinical Neurosurgery. 2004;51:228–237. - PubMed
    1. Nakaji P, Speltzer RF. The Marriage of Technique, Technology, and Judgement. Innovations in Surgical Approach. 2004;51:177–185. - PubMed
    1. Christensen GE, Johnson HJ. Consistent Image Registration. IEEE Transactions on Medical Imaging. 2001;20(7):568–582. - PubMed
    1. Warfield SK, et al. Real-time biomechanical simulation of volumetric brain deformation for image guided neurosurgery. SC 2000: High Performance Networking and Computing Conference; USA: Dallas; 2000.
    1. Clatz O, et al. Patient Specific Biomechanical Model of the Brain: Application to Parkinson’s disease procedure. International Symposium on Surgery Simulation and Soft Tissue Modeling (IS4TM’03); Juan-les-Pins, France: Springer-Verlag; 2003.

LinkOut - more resources