On Quantifying Local Geometric Structures of Fiber Tracts

Jian Cheng (1, 2, 3) , Tao Liu (1) , Feng Shi (4) , Ruiliang Bai (5) , Jicong Zhang (1) , Haogang Zhu (1) , Dacheng Tao (6) , Peter Basser (3)

1 BUAA - Beihang University
2 The University of Sydney
3 NIH - National Institutes of Health [Bethesda, MD, USA]
4 Shanghai United Imaging Intelligence Co., Ltd.
5 Zhejiang University
6 University of Sydney

In diffusion MRI, fiber tracts, represented by densely distributed 3D curves, can be estimated from diffusion weighted images using tractography. The spatial geometric structure of white matter fiber tracts is known to be complex in human brain, but it carries intrinsic information of human brain. In this paper, inspired by studies of liquid crystals, we propose tract-based director field analysis (tDFA) with total six rotationally invariant scalar indices to quantify local geometric structures of fiber tracts. The contributions of tDFA include: 1) We propose orientational order (OO) and orientational dispersion (OD) indices to quantify the degree of alignment and dispersion of fiber tracts; 2) We define the local orthogonal frame for a set of unoriented curves, which is proved to be a generalization of the Frenet frame defined for a single oriented curve; 3) With the local orthogonal frame, we propose splay, bend, and twist indices to quantify three types of orientational distortion of local fiber tracts, and a total distortion index to describe distortions of all three types. The proposed tDFA for fiber tracts is a generalization of the voxel-based DFA (vDFA) which was recently proposed for a spherical function field (i.e., an ODF field). To our knowledge, this is the first work to quantify orientational distortion (splay, bend, twist, and total distortion) of fiber tracts. Experiments show that the proposed scalar indices are useful descriptors of local geometric structures to visualize and analyze fiber tracts.

Domaines: Imagerie médicale

Jian Cheng, Tao Liu, Feng Shi, Ruiliang Bai, Jicong Zhang, et al.. On Quantifying Local Geometric Structures of Fiber Tracts. Medical Image Computing and Computer-Assisted Intervention, Sep 2018, Granada, Spain. pp.392-400, ⟨10.1007/978-3-030-00931-1_45⟩. ⟨hal-02098668⟩