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

Grand Roman Joldes, Adam Wittek, Mathieu Couton, Simon K. Warfield and Karol Miller
Intelligent Systems for Medicine Laboratory, The University of Western Australia, 35 Stirling Highway, 6009 Crawley/Perth, Western Australia, Australia
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009, Lecture Notes in Computer Science, 2009, Volume 5762/2009, 300-307


   title={Real-time prediction of brain shift using nonlinear finite element algorithms},

   author={Joldes, G. and Wittek, A. and Couton, M. and Warfield, S. and Miller, K.},

   journal={Medical Image Computing and Computer-Assisted Intervention–MICCAI 2009},





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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.
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