Canonical polyadic decomposition for tissue type differentiation using multi-parametric MRI in high-grade gliomas

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.