Framework for shape analysis of white matter fiber bundles

• Published in: NeuroImage (Orlando, Fla.) Vol. 167; pp. 466 - 477
• Main Authors: Glozman, Tanya, Bruckert, Lisa, Pestilli, Franco, Yecies, Derek W., Guibas, Leonidas J., Yeom, Kristen W.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.02.2018; Elsevier Limited
• Subjects: Adult; Age; Between-subjects design; Child; Child, Preschool; Children; Data compression; Datasets; Development; Diffusion Magnetic Resonance Imaging - methods; Diffusion Tensor Imaging - methods; Female; Geometry; Humans; Image Processing, Computer-Assisted - methods; Infant; Male; Models, Theoretical; Nerve Fibers, Myelinated; Neuroimaging; NMR; Nuclear magnetic resonance; Pediatrics; Registration; Shape analysis; Substantia alba; White matter; White Matter - diagnostic imaging; Development; White matter; Shape analysis
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2017.11.052

Diffusion imaging coupled with tractography algorithms allows researchers to image human white matter fiber bundles in-vivo. These bundles are three-dimensional structures with shapes that change over time during the course of development as well as in pathologic states. While most studies on white matter variability focus on analysis of tissue properties estimated from the diffusion data, e.g. fractional anisotropy, the shape variability of white matter fiber bundle is much less explored. In this paper, we present a set of tools for shape analysis of white matter fiber bundles, namely: (1) a concise geometric model of bundle shapes; (2) a method for bundle registration between subjects; (3) a method for deformation estimation. Our framework is useful for analysis of shape variability in white matter fiber bundles. We demonstrate our framework by applying our methods on two datasets: one consisting of data for 6 normal adults and another consisting of data for 38 normal children of age 11 days to 8.5 years. We suggest a robust and reproducible method to measure changes in the shape of white matter fiber bundles. We demonstrate how this method can be used to create a model to assess age-dependent changes in the shape of specific fiber bundles. We derive such models for an ensemble of white matter fiber bundles on our pediatric dataset and show that our results agree with normative human head and brain growth data. Creating these models for a large pediatric longitudinal dataset may improve understanding of both normal development and pathologic states and propose novel parameters for the examination of the pediatric brain.