Combining white matter diffusion and geometry for tract-specific alignment and variability analysis

• Published in: NeuroImage (Orlando, Fla.) Fla.), 2019-10, Vol.200, p.674-689
• Main Authors: Benou, Itay, Veksler, Ronel, Friedman, Alon, Raviv, Tammy Riklin
• Format: Article
• Language: English
• Subjects: Adult; Algorithms; Athletic Injuries - diagnostic imaging; Athletic Injuries - pathology; Brain Concussion - diagnostic imaging; Brain Concussion - pathology; Diffusion MRI; Diffusion Tensor Imaging - methods; Fiber bundle; Geometry; Humans; Magnetic resonance imaging; Male; Methods; Mild traumatic brain injury; Neuroimaging; NMR; Nuclear magnetic resonance; Registration; Statistical analysis; Substantia alba; Tract-specific analysis; Tractography registration; Traumatic brain injury; White matter; White Matter - anatomy & histology; White Matter - diagnostic imaging
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2019.05.003

We present a framework for along-tract analysis of white matter (WM) fiber bundles based on diffusion tensor imaging (DTI) and tractography. We introduce the novel concept of fiber-flux density for modeling fiber tracts’ geometry, and combine it with diffusion-based measures to define vector descriptors called Fiber-Flux Diffusion Density (FFDD). The proposed model captures informative features of WM tracts at both the microscopic (diffusion-related) and macroscopic (geometry-related) scales, thus enabling improved sensitivity to subtle structural abnormalities that are not reflected by either diffusion or geometrical properties alone. A key step in this framework is the construction of an FFDD dissimilarity measure for sub-voxel alignment of fiber bundles, based on the fast marching method (FMM). The obtained aligned WM tracts enable meaningful inter-subject comparisons and group-wise statistical analysis. Moreover, we show that the FMM alignment can be generalized in a straight forward manner to a single-shot co-alignment of multiple fiber bundles. The proposed alignment technique is shown to outperform a well-established, commonly used DTI registration algorithm. We demonstrate the FFDD framework on the Human Connectome Project (HCP) diffusion MRI dataset, as well as on two different datasets of contact sports players. We test our method using longitudinal scans of a basketball player diagnosed with a traumatic brain injury, showing compatibility with structural MRI findings. We further perform a group study comparing mid- and post-season scans of 13 active football players exposed to repetitive head trauma, to 17 non-player control (NPC) subjects. Results reveal statistically significant FFDD differences (p-values