Tensor Based Blind Source Separation in Longitudinal Magnetic Resonance Imaging Analysis.

Abstract

Study of white matter (WM) fiber-bundles is a crucial challenge in the investigation of neurological diseases like multiple sclerosis (MS). In this activity, the amount of data to process is huge, and an automated approach to carry out this task is in order.In this paper we show how tensor-based blind source separation (BSS) techniques can be successfully applied to model complex anatomical brain structures. More in detail, we show how through vector hankelization it is possible to formalize data extracted from WM fiber-bundles using a tensor model. Two main tensor factorization techniques, namely (Lr, Lr, 1) block term decomposition (BTD) and canonical polyadic decomposition (CPD), were applied to the generated tensor. The information extracted from the factorization was then used to differentiate between sets of fibers, within the bundle, affected by the pathology and normal appearing fibers.Performances of the proposed tensor-based model was evaluated on simulated data representing pathological effects of MS. Results show the capability of our tensor-based model to detect small pathological phenomena appearing along WM fibers.