Real-Time Prediction of Brain Shift Using Nonlinear Finite Element Algorithms

Authors:
Grand Roman Joldes;Adam Wittek;Mathieu Couton;Simon K. Warfield;Karol Miller
Affiliations:
Intelligent Systems for Medicine Laboratory, The University of Western Australia, Crawley/Perth, Western Australia, Australia 6009;Intelligent Systems for Medicine Laboratory, The University of Western Australia, Crawley/Perth, Western Australia, Australia 6009;Intelligent Systems for Medicine Laboratory, The University of Western Australia, Crawley/Perth, Western Australia, Australia 6009 and Institut Francais de Mechanique Avancee IFMA, Aubiere Cedex, ...;Computational Radiology Laboratory, Children's Hospital Boston and Harvard Medical School, Boston, USA MA02115;Intelligent Systems for Medicine Laboratory, The University of Western Australia, Crawley/Perth, Western Australia, Australia 6009
Venue:
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Year:
2009

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Abstract

Patient-specific biomechanical models implemented using specialized nonlinear (i.e. taking into account material and geometric nonlinearities) finite element procedures were applied to predict the deformation field within the brain for five cases of craniotomy-induced brain shift. The procedures utilize the Total Lagrangian formulation with explicit time stepping. The loading was defined by prescribing deformations on the brain surface under the craniotomy. Application of the computed deformation fields to register the preoperative images with the intraoperative ones indicated that the models very accurately predict the intraoperative positions and deformations of the brain anatomical structures for limited information about the brain surface deformations. For each case, it took less than 40 s to compute the deformation field using a standard personal computer, and less than 4 s using a Graphics Processing Unit (GPU). The results suggest that nonlinear biomechanical models can be regarded as one possible method of complementing medical image processing techniques when conducting non-rigid registration within the real-time constraints of neurosurgery.