Paper Info
Title
Canonical polyadic decomposition for tissue type differentiation using multi-parametric MRI in high-grade gliomas.
Abstract
In diagnosis and treatment planning of brain tumors, characterisation and localization of tissue plays an important role. Blind source separation techniques are generally employed to extract the tissue-specific profiles and its corresponding distribution from the multi-parametric MRI. A 3-dimensional tensor is constructed from in-vivo multi-parametric MRI of high grade glioma patients. Constrained canonical polyadic decomposition (CPD) with common factor in mode-1 and mode-2 and l(1) regularization on mode-3 is applied on the 3-dimensional multi-parametric tensor to characterize various tissue types. An initial in-vivo study shows that CPD has slightly better performance in identifying active tumor and the tumor core region in high-grade glioma patients compared to hierarchical non-negative matrix factorization.
Year
Venue
Field
2016
European Signal Processing Conference
Tensor,Glioma,Matrix decomposition,High-Grade Glioma,Parametric statistics,Regularization (mathematics),Blind signal separation,Nuclear magnetic resonance,Mathematics,Magnetic resonance imaging
DocType
ISSN
Citations 
Conference
2076-1465
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
H. N. Bharath101.01
N. Sauwen211.70
Diana Maria Sima300.68
Uwe Himmelreich41029.60
Lieven De Lathauwer53002226.72
Sabine Van Huffel61058149.38