Title
High-grade glioma diffusive modeling using statistical tissue information and diffusion tensors extracted from atlases.
Abstract
Glioma, especially glioblastoma, is a leading cause of brain cancer fatality involving highly invasive and neoplastic growth. Diffusive models of glioma growth use variations of the diffusion-reaction equation in order to simulate the invasive patterns of glioma cells by approximating the spatiotemporal change of glioma cell concentration. The most advanced diffusive models take into consideration the heterogeneous velocity of glioma in gray and white matter, by using two different discrete diffusion coefficients in these areas. Moreover, by using diffusion tensor imaging (DTI), they simulate the anisotropic migration of glioma cells, which is facilitated along white fibers, assuming diffusion tensors with different diffusion coefficients along each candidate direction of growth. Our study extends this concept by fully exploiting the proportions of white and gray matter extracted by normal brain atlases, rather than discretizing diffusion coefficients. Moreover, the proportions of white and gray matter, as well as the diffusion tensors, are extracted by the respective atlases; thus, no DTI processing is needed. Finally, we applied this novel glioma growth model on real data and the results indicate that prognostication rates can be improved.
Year
DOI
Venue
2012
10.1109/TITB.2011.2171190
IEEE Transactions on Information Technology in Biomedicine
Keywords
Field
DocType
computer simulation,diffusion coefficient,tensile stress,cancer,diffusion tensor imaging,mathematical model,diffusion tensor,statistical analysis
Discretization,Diffusion MRI,Brain atlas,Anisotropy,White matter,Biological system,Tensor,Computer science,Artificial intelligence,Pathology,Computer vision,Glioma,High-Grade Glioma
Journal
Volume
Issue
ISSN
16
2
1558-0032
Citations 
PageRank 
References 
7
0.94
5
Authors
6