Towards population based characterization of neuronal fiber pathways with diffusion tensor imaging

Abstract

Diffusion tensor imaging has been widely used to reconstruct neuronal fibers in the human brain. Studying these fibers often requires them to be grouped into bundles that correspond to coherent anatomic structures. Several fiber bundling methods are proposed and evaluated in this work. A unified fiber bundling and registration algorithm, which refers to a pre-built bundle template, is firstly proposed to provide fiber bundling consistent with well-defined major white matter pathways. Furthermore, a clustering algorithm, which is constrained by a cortex parcellation, is proposed to automatically segment connections between cortical/sub-cortical areas. Based on this framework, a group-wise fiber bundling method is further proposed to leverage a group of DTI data for improving across subject bundle consistency. The above methods have been rigorously evaluated with in vivo DTI data, demonstrating a potential of being used to better characterize white matter pathways and measure the connectivity.