Patient-Specific Non-Linear Finite Element Modelling for Predicting Soft Organ Deformation in Real-Time; Application to Non-Rigid Neuroimage Registration

Adam Wittek, Grand Joldes, Mathieu Couton, Simon K. Warfield, Karol Miller
Intelligent Systems for Medicine Laboratory, School of Mechanical and Chemical Engineering, The University of Western Australia, 35 Stirling Highway, Crawley WA 6009, Australia
Progress in Biophysics and Molecular Biology (22 September 2010)


   title={Patient-Specific Non-Linear Finite Element Modelling for Predicting Soft Organ Deformation in Real-Time; Application to Non-Rigid Neuroimage Registration1},

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

   journal={Progress in Biophysics and Molecular Biology},





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Long computation times of non-linear (i.e. accounting for geometric and material non-linearity) biomechanical models have been regarded as one of the key factors preventing application of such models in predicting organ deformation for image-guided surgery. This contribution presents real-time patient-specific computation of the deformation field within the brain for six cases of brain shift induced by craniotomy (i.e. surgical opening of the skull) using specialised non-linear finite element procedures implemented on a graphics processing unit (GPU). In contrast to commercial finite element codes that rely on an updated Lagrangian formulation and implicit integration in time domain for steady state solutions, our procedures utilise the total Lagrangian formulation with explicit time stepping and dynamic relaxation. We used patient-specific finite element meshes consisting of hexahedral and non-locking tetrahedral elements, together with realistic material properties for the brain tissue and appropriate contact conditions at the boundaries. The loading was defined by prescribing deformations on the brain surface under the craniotomy. Application of the computed deformation fields to register (i.e. align) the preoperative and intraoperative images indicated that the models very accurately predict the intraoperative deformations within the brain. For each case, computing the brain deformation field took less than 4 s using a NVIDIA Tesla C870 GPU, which is two orders of magnitude reduction in computation time in comparison to our previous study in which the brain deformation was predicted using a commercial finite element solver executed on a personal computer.
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