NDLI: Comparing finite elements and finite differences for developing diffusive models of glioma growth

Content Provider	IEEE Xplore Digital Library
Author	Roniotis, A. Marias, K. Sakkalis, V. Stamatakos, G. Zervakis, M.
Copyright Year	2010
Description	Author affiliation: Institute of Communication and Computer Systems, Dept of Electrical and Computer Engineering, Technical University of Athens, 15780, Greece (Stamatakos, G.; Zervakis, M.) \|\| Institute of Computer Science, Foundation for Research and Technology (FORTH), Heraklion 71110, Greece (Roniotis, A.; Marias, K.; Sakkalis, V.)
Abstract	Glioma is the most aggressive type of brain tumor. Several mathematical models have been developed during the last two decades, towards simulating the mechanisms that govern the development of glioma. The most common models use the diffusion-reaction equation (DRE) for simulating the spatiotemporal variation of tumor cell concentration. The proposed diffusive models have mainly used finite differences (FDs) or finite elements (FEs) for the approximation of the solution of the partial differential DRE. This paper presents experimental results on the comparison of the FEs and FDs, especially focused on the glioma model case. It is studied how the different meshes of brain can affect computational consistency, simulation time and efficiency of the model. The experiments have been studied on a test case, for which there is a known algebraic expression of the solution. Thus, it is possible to calculate the error that the different models yield.
Starting Page	6797
Ending Page	6800
File Size	964077
Page Count	4
File Format	PDF
ISBN	9781424441235
ISSN	1557170X
DOI	10.1109/IEMBS.2010.5625973
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-31
Publisher Place	Argentina
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Mathematical model Finite element methods Computational modeling Brain models Numerical models Equations
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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