An Optimised Linear Mechanical Model for Estimating Brain Shift Caused by Meningioma Tumours

Hossein Yousefi; Alireza Ahmadian; Davood Khodadad; Hooshangh Saberi; Alireza Daneshmehr

doi:doi:10.11648/j.ijbse.20130101.11

| Peer-Reviewed

An Optimised Linear Mechanical Model for Estimating Brain Shift Caused by Meningioma Tumours

Hossein Yousefi, Alireza Ahmadian, Davood Khodadad, Hooshangh Saberi, Alireza Daneshmehr

Published in International Journal of Biomedical Science and Engineering (Volume 1, Issue 1)

Received: 28 April 2013 Published: 10 June 2013

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

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%.

Published in	International Journal of Biomedical Science and Engineering (Volume 1, Issue 1)
DOI	10.11648/j.ijbse.20130101.11
Page(s)	1-9
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2013. Published by Science Publishing Group

Keywords

Brain Deformation, Finite Element Modelling, Linear and Non-Linear Brain Models, Brain Tumour, Tumour Growth

References

[1]	Oden JT, Research directions in computational mechanics. Computer Methods in Applied Mechanics and Engineering 2003; 192: 913-22.
[2]	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
[3]	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.
[4]	Hogea CS, Davatzikos C, Birosb G. Brain-Tumor Interaction Biophysical Models for Medical Image Registration. 2008. doi./10.1137/07069208X
[5]	Clatz O, Bondiau PY, Delingette H, et al. Brain Tumor Growth Simulation. IRNA; 2004. Report No.: 5187.
[6]	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.
[7]	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.
[8]	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.
[9]	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.
[10]	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.
[11]	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.
[12]	Park BJ, Kim HU, Sade B, et al. Meningiomas Diagnosis, Treatment and Outcome. Doi: 10.1007/978-1-84628-784-8 Springer2008.31-65
[13]	Kaus M, Warfeld SK, Nabavi A, et al. Automated Segmentation of MRI of Brain Tumors. Radiology 2001; 218(2): 586-91.
[14]	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.
[15]	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.
[16]	Miller K, Taylor Z, Wittek A. Mathematical models of brain deformation behaviour for computer-integrated neurosurgery. Research Report # ISML/01/2006
[17]	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.
[18]	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
[19]	Wasserman R, Acharya R. A patient-specificthe in vivo tumor model.Mathematical Biosciences1996; 136(2): 111-40.
[20]	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
[21]	www.brainweb.com 2011.
[22]	www.3Dslicer.org. 2011.

Cite This Article

Plain Text BibTeX RIS

APA Style

Hossein Yousefi, Alireza Ahmadian, Davood Khodadad, Hooshangh Saberi, Alireza Daneshmehr. (2013). An Optimised Linear Mechanical Model for Estimating Brain Shift Caused by Meningioma Tumours. International Journal of Biomedical Science and Engineering, 1(1), 1-9. https://doi.org/10.11648/j.ijbse.20130101.11

Copy | Download

ACS Style

Hossein Yousefi; Alireza Ahmadian; Davood Khodadad; Hooshangh Saberi; Alireza Daneshmehr. An Optimised Linear Mechanical Model for Estimating Brain Shift Caused by Meningioma Tumours. Int. J. Biomed. Sci. Eng. 2013, 1(1), 1-9. doi: 10.11648/j.ijbse.20130101.11

Copy | Download

AMA Style

Hossein Yousefi, Alireza Ahmadian, Davood Khodadad, Hooshangh Saberi, Alireza Daneshmehr. An Optimised Linear Mechanical Model for Estimating Brain Shift Caused by Meningioma Tumours. Int J Biomed Sci Eng. 2013;1(1):1-9. doi: 10.11648/j.ijbse.20130101.11

Copy | Download

@article{10.11648/j.ijbse.20130101.11,
  author = {Hossein Yousefi and Alireza Ahmadian and Davood Khodadad and Hooshangh Saberi and Alireza Daneshmehr},
  title = {An Optimised Linear Mechanical Model for Estimating Brain Shift Caused by Meningioma Tumours},
  journal = {International Journal of Biomedical Science and Engineering},
  volume = {1},
  number = {1},
  pages = {1-9},
  doi = {10.11648/j.ijbse.20130101.11},
  url = {https://doi.org/10.11648/j.ijbse.20130101.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijbse.20130101.11},
  abstract = {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%.},
 year = {2013}
}

Copy | Download

TY  - JOUR
T1  - An Optimised Linear Mechanical Model for Estimating Brain Shift Caused by Meningioma Tumours
AU  - Hossein Yousefi
AU  - Alireza Ahmadian
AU  - Davood Khodadad
AU  - Hooshangh Saberi
AU  - Alireza Daneshmehr
Y1  - 2013/06/10
PY  - 2013
N1  - https://doi.org/10.11648/j.ijbse.20130101.11
DO  - 10.11648/j.ijbse.20130101.11
T2  - International Journal of Biomedical Science and Engineering
JF  - International Journal of Biomedical Science and Engineering
JO  - International Journal of Biomedical Science and Engineering
SP  - 1
EP  - 9
PB  - Science Publishing Group
SN  - 2376-7235
UR  - https://doi.org/10.11648/j.ijbse.20130101.11
AB  - 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%.
VL  - 1
IS  - 1
ER  -

Copy | Download

Author Information

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, Lule? University of Technology, SE-971 87 Lule?, 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

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Hossein Yousefi, Alireza Ahmadian, Davood Khodadad, Hooshangh Saberi, Alireza Daneshmehr. (2013). An Optimised Linear Mechanical Model for Estimating Brain Shift Caused by Meningioma Tumours. International Journal of Biomedical Science and Engineering, 1(1), 1-9. https://doi.org/10.11648/j.ijbse.20130101.11

Copy | Download

ACS Style

Hossein Yousefi; Alireza Ahmadian; Davood Khodadad; Hooshangh Saberi; Alireza Daneshmehr. An Optimised Linear Mechanical Model for Estimating Brain Shift Caused by Meningioma Tumours. Int. J. Biomed. Sci. Eng. 2013, 1(1), 1-9. doi: 10.11648/j.ijbse.20130101.11

Copy | Download

AMA Style

Hossein Yousefi, Alireza Ahmadian, Davood Khodadad, Hooshangh Saberi, Alireza Daneshmehr. An Optimised Linear Mechanical Model for Estimating Brain Shift Caused by Meningioma Tumours. Int J Biomed Sci Eng. 2013;1(1):1-9. doi: 10.11648/j.ijbse.20130101.11

Copy | Download

@article{10.11648/j.ijbse.20130101.11,
  author = {Hossein Yousefi and Alireza Ahmadian and Davood Khodadad and Hooshangh Saberi and Alireza Daneshmehr},
  title = {An Optimised Linear Mechanical Model for Estimating Brain Shift Caused by Meningioma Tumours},
  journal = {International Journal of Biomedical Science and Engineering},
  volume = {1},
  number = {1},
  pages = {1-9},
  doi = {10.11648/j.ijbse.20130101.11},
  url = {https://doi.org/10.11648/j.ijbse.20130101.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijbse.20130101.11},
  abstract = {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%.},
 year = {2013}
}

Copy | Download

TY  - JOUR
T1  - An Optimised Linear Mechanical Model for Estimating Brain Shift Caused by Meningioma Tumours
AU  - Hossein Yousefi
AU  - Alireza Ahmadian
AU  - Davood Khodadad
AU  - Hooshangh Saberi
AU  - Alireza Daneshmehr
Y1  - 2013/06/10
PY  - 2013
N1  - https://doi.org/10.11648/j.ijbse.20130101.11
DO  - 10.11648/j.ijbse.20130101.11
T2  - International Journal of Biomedical Science and Engineering
JF  - International Journal of Biomedical Science and Engineering
JO  - International Journal of Biomedical Science and Engineering
SP  - 1
EP  - 9
PB  - Science Publishing Group
SN  - 2376-7235
UR  - https://doi.org/10.11648/j.ijbse.20130101.11
AB  - 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%.
VL  - 1
IS  - 1
ER  -

Copy | Download