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

摘要

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 l1 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.