4th Order Symmetric Tensors and Positive ADC Modelling

Aurobrata Ghosh (1) , Rachid Deriche (1)

1 ATHENA - Computational Imaging of the Central Nervous System

High Order Cartesian Tensors (HOTs) were introduced in Generalized DTI (GDTI) to overcome the limitations of DTI. HOTs can model the apparent diffusion coefficient (ADC) with greater accuracy than DTI in regions with fiber heterogeneity. Although GDTI HOTs were designed to model positive diffusion, the straightforward least square (LS) estimation of HOTs doesn't guarantee positivity. In this chapter we address the problem of estimating 4th order tensors with positive diffusion profiles. Two known methods exist that broach this problem, namely a Riemannian approach based on the algebra of 4th order tensors, and a polynomial approach based on Hilbert's theorem on non-negative ternary quartics. In this chapter, we review the technicalities of these two approaches, compare them theoretically to show their pros and cons, and compare them against the Euclidean LS estimation on synthetic, phantom and real data to motivate the relevance of the positive diffusion profile constraint.

Mots clés: Cartesian Tensors; Generalized DTI; ADC; Diffusion MRI; Ternary Quartics; Riemannian Metric; Positivity Constraint

Domaines: Imagerie médicale

Aurobrata Ghosh, Rachid Deriche. 4th Order Symmetric Tensors and Positive ADC Modelling. Anna Vilanova and Carl-Fredrik Westin and Bernhard Burgeth. Visualization and Processing of Tensors and Higher Order Descriptors for Multi-Valued Data (Dagstuhl Seminar 11501), Springer, 2013. ⟨hal-00848513⟩