Simulation of Brain Mass Effect with an Arbitrary Lagrangian and Eulerian FEM

Estimation of intracranial stress distribution caused by mass effect is critical to the management of hemorrhagic stroke or brain tumor patients, who may suffer severe secondary brain injury from brain tissue compression. Coupling with physiological parameters that are readily available using MRI, eg, tissue perfusion, a non-invasive, quantitative and regional estimation of intracranial stress distribution could offer a better understanding of brain tissue's reaction under mass effect. A quantitative and sound measurement serving this particular purpose remains elusive due to multiple challenges associated with biomechanical modeling of the brain. One such challenge for the conventional Lagrangian frame based finite element method (LFEM) is that the mesh distortion resulted from the expansion of the mass effects can terminate the simulation prematurely before the desired pressure loading is achieved. In this work, we adopted an arbitrary Lagrangian and Eulerian FEM method (ALEF) with explicit dynamic solutions to simulate the expansion of brain mass effects caused by a pressure loading. This approach consists of three phases: 1) a Lagrangian phase to deform mesh like LFEM, 2) a mesh smoothing phase to reduce mesh distortion, and 3) an Eulerian phase to map the state variables from the old mesh to the smoothed one. In 2D simulations with simulated geometries, this approach is able to model substantially larger deformations compared to LFEM. We further applied this approach to a simulation with 3D real brain geometry to quantify the distribution of von Mises stress within the brain.

[1]  B. Vanleer,et al.  Towards the ultimate conservative difference scheme. IV. A new approach to numerical convection , 1977 .

[2]  Alain Trouvé,et al.  Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms , 2005, International Journal of Computer Vision.

[3]  Christos Davatzikos,et al.  A Comparative Study of Biomechanical Simulators in Deformable Registration of Brain Tumor Images , 2008, IEEE Transactions on Biomedical Engineering.

[4]  Christos Davatzikos,et al.  A robust framework for soft tissue simulations with application to modeling brain tumor mass effect in 3D MR images , 2007, Physics in medicine and biology.

[5]  K. Chinzei,et al.  Mechanical properties of brain tissue in tension. , 2002, Journal of biomechanics.

[6]  B. V. Leer,et al.  Towards the ultimate conservative difference scheme. IV. A new approach to numerical convection , 1977 .

[7]  Hervé Delingette,et al.  Robust nonrigid registration to capture brain shift from intraoperative MRI , 2005, IEEE Transactions on Medical Imaging.

[8]  Dinggang Shen,et al.  HAMMER: hierarchical attribute matching mechanism for elastic registration , 2002, IEEE Transactions on Medical Imaging.

[9]  Karol Miller,et al.  Patient-specific model of brain deformation: application to medical image registration. , 2007, Journal of biomechanics.

[10]  Wing Kam Liu,et al.  Nonlinear Finite Elements for Continua and Structures , 2000 .

[11]  A. Hartov,et al.  Model-updated image guidance: initial clinical experiences with gravity-induced brain deformation , 2002, 5th IEEE EMBS International Summer School on Biomedical Imaging, 2002..

[12]  B. V. Leer,et al.  Towards the Ultimate Conservative Difference Scheme , 1997 .