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An Optimised Linear Mechanical Model for Estimating Brain Shift Caused by Meningioma Tumours
Tehran University of Medical Sciences (TUMS), Iran.
Tehran University of Medical Sciences (TUMS), Iran.
LuleƄ University of Technology ; Exceptional Talents Development Centre, Iran. (Signals and Waves)ORCID iD: 0000-0003-2960-3094
Tehran University of Medical Sciences (TUMS), Iran ; Spinal Injuries Repair Research Centre, Iran.
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2013 (English)In: International Journal of Biomedical Science and Engineering, ISSN 2376-7227, Vol. 1, no 1, p. 1-9Article in journal (Refereed) Published
Abstract [en]

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%.

Place, publisher, year, edition, pages
2013. Vol. 1, no 1, p. 1-9
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:lnu:diva-55803DOI: 10.11648/j.ijbse.20130101.11OAI: oai:DiVA.org:lnu-55803DiVA, id: diva2:956011
Available from: 2016-08-29 Created: 2016-08-29 Last updated: 2018-09-10Bibliographically approved

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Publisher's full texthttp://www.sciencepublishinggroup.com/journal/paperinfo.aspx?journalid=259&doi=10.11648/j.ijbse.20130101.11

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Khodadad, Davood

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CiteExportLink to record
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