An Optimised Linear Mechanical Model for Estimating Brain Shift Caused by Meningioma Tumours
International Journal of Biomedical Science and Engineering
Volume 1, Issue 1, June 2013, Pages: 1-9
Received: Apr. 28, 2013;
Published: Jun. 10, 2013
Views 3685 Downloads 156
Hossein Yousefi, Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences (TUMS), Research Centre for Biomedical Technology and Robotics, RCBTR, Tehran, Iran
Alireza Ahmadian, Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences (TUMS), Research Centre for Biomedical Technology and Robotics, RCBTR, Tehran, Iran
Davood Khodadad, Experimental Mechanics, Luleå University of Technology, SE-971 87 Luleå, Sweden, Exceptional Talents Development Centre,Tehran, Iran
Hooshangh Saberi, Department of Neurosurgery, Tehran University of Medical Sciences (TUMS), Brain and Spinal Injuries Repair Research Centre, Tehran, Iran
Alireza Daneshmehr, Department of Mechanical Engineering, University of Tehran, Iran
Estimation of brain deformation plays an important role in computer-aided therapy and image-guided neurosurgery systems. Tumour growth can cause brain deformation and change stress distribution in the brain. Biomechanical models exist that use a finite element method to estimate brain shift caused by tumour growth. Such models can be categorised as linear and non-linear models, both of which assume finite deformation of the brain after tumour growth. Linear models are easy to implement and fast enough to for applications such as IGS where the time is a great of concern. However their accuracy highly dependent on the parameters of the models in this paper, we proposed an optimisation approach to improve a naive linear model to achieve more precise estimation of brain displacements caused by tumour growth. The optimisation process has improved the accuracy of the model by adapting the brain model parameters according to different tomour sizes.We used patient-based tetrahedron finite element mesh with proper material properties for brain tissue and appropriate boundary conditions in the tumour region. Anatomical landmarks were determined by an expert and were divided into two different sets for evaluation and optimisation. Tetrahedral finite element meshes were used and the model parameters were optimised by minimising the mean square distance between the predicted locations of the anatomical landmarks derived from Brain Atlas images and their actual locations on the tumour images. Our results demonstrate great improvement in the accuracy of an optimised linear mechanical model that achieved an accuracy rate of approximately 92%.
An Optimised Linear Mechanical Model for Estimating Brain Shift Caused by Meningioma Tumours, International Journal of Biomedical Science and Engineering.
Vol. 1, No. 1,
2013, pp. 1-9.
Oden JT, Research directions in computational mechanics. Computer Methods in Applied Mechanics and Engineering 2003; 192: 913-22.
Miller K, Wittek A, Joldes R, et al. Modelling brain deformations for computer-integrated neurosurgery. Communications In Numerical Methods In Engineering 2009. DOI: 10.1002/cnm.1260
Hamidian H, Soltanian-Zadeh H, Faraji-Dana R, et al Estimating Brain Deformation During Surgery Using Finite Element Method: Optimization and Comparison of Two LinearModels. Springer Science 2008; 55: 157-67.
Hogea CS, Davatzikos C, Birosb G. Brain-Tumor Interaction Biophysical Models for Medical Image Registration. 2008. doi./10.1137/07069208X
Clatz O, Bondiau PY, Delingette H, et al. Brain Tumor Growth Simulation. IRNA; 2004. Report No.: 5187.
Hogea CS, Birosb G, Davatzikos C. Fast Solvers for Soft Tissue Simulation with Application to Construction of Brain Tumor Atlases urll ww.seas.upenn.edu/~biros/papers/brain06.pdf l. 2007.
Mohamed A, Zacharaki EI, Shen D, et al. Deformable registration of brain tumor images via a statistical model of tumor-induced deformation. Medical Image Analysis 2006; 10: 752-63.
Clatz O, Sermesant M, Bondiau PY, et al. Realistic Simulation of the 3D Growth of Brain Tumors in MR Images Coupling Diffusion with Biomechanical Deformation. IEEE Trans Med Imaging 2005; 24: 1334-46.
Zacharaki EI, Hogea CS, Shen D, et al. Non-diffeomorphic registration of brain tumor images by simulating tissue loss and tumor growth. NeuroImage 2009; 46: 762-74.
Powathil G, Kohandel M, Sivaloganathan S, et al. Mathematical modeling of brain tumors: effects of radiotherapy and chemotherapy. Physics in medicine and biology 2007;52: 3291-306.
Bach Cuadra M, De Craeneb M, Duaya V, et al. Dense deformation field estimation for atlas-based segmentation of pathological MR brain images. computer methods and programs in biomedicine 2006; 84: 66-75.
Park BJ, Kim HU, Sade B, et al. Meningiomas Diagnosis, Treatment and Outcome. Doi: 10.1007/978-1-84628-784-8 Springer2008.31-65
Kaus M, Warfeld SK, Nabavi A, et al. Automated Segmentation of MRI of Brain Tumors. Radiology 2001; 218(2): 586-91.
Warfeld SK, Ferrant M, Gallez X, et al. Real-Time Biomechanical Simulationof Volumetric Brain Deformation for Image Guided Neurosurgery. IEEE transactions on Medical Imaging 2000 0-7803-9802-5.
Ferrant M, Nabavi A, Macq B, et al. Registration of 3-D intraoperative MR images of the brain using a finite element biomechanical model. IEEE transactions on Medical Imaging 2001; 20: 1384-97.
Miller K, Taylor Z, Wittek A. Mathematical models of brain deformation behaviour for computer-integrated neurosurgery. Research Report # ISML/01/2006
Wittek A, Miller K, Kikinis R, et al. Patient-specific model of brain deformation: Application tomedical image registration. elsevier Journal of Biomechanics. doi:10.1016/j.jbiomech.2006.02.021.
Yousefi H. Ahmadian A, Saberi H, et al. "Brain tumor modeling: glioma growth and interaction with chemotherapy",Proc. SPIE 8285, International Conference on Graphic and Image Processing (ICGIP 2011), 82851M doi:10.1117/12.913432
Wasserman R, Acharya R. A patient-specificthe in vivo tumor model.Mathematical Biosciences1996; 136(2): 111-40.
Khodadad D; Ahmadian A; Ay M; et al."B-spline based free form deformation thoracic non-rigid registration of CT and PET images", Proc. SPIE 8285, International Conference on Graphic and Image Processing (ICGIP 2011), 82851K doi:10.1117/12.913422