Diffusion tensor imaging tensor shape analysis for assessment of regional white matter differences.

Published

Journal Article

Purpose The purpose of this study was to investigate a novel tensor shape plot analysis technique of diffusion tensor imaging data as a means to assess microstructural differences in brain tissue. We hypothesized that this technique could distinguish white matter regions with different microstructural compositions. Methods Three normal canines were euthanized at seven weeks old. Their brains were imaged using identical diffusion tensor imaging protocols on a 7T small-animal magnetic resonance imaging system. We examined two white matter regions, the internal capsule and the centrum semiovale, each subdivided into an anterior and posterior region. We placed 100 regions of interest in each of the four brain regions. Eigenvalues for each region of interest triangulated onto tensor shape plots as the weighted average of three shape metrics at the plot's vertices: CS, CL, and CP. Results The distribution of data on the plots for the internal capsule differed markedly from the centrum semiovale data, thus confirming our hypothesis. Furthermore, data for the internal capsule were distributed in a relatively tight cluster, possibly reflecting the compact and parallel nature of its fibers, while data for the centrum semiovale were more widely distributed, consistent with the less compact and often crossing pattern of its fibers. This indicates that the tensor shape plot technique can depict data in similar regions as being alike. Conclusion Tensor shape plots successfully depicted differences in tissue microstructure and reflected the microstructure of individual brain regions. This proof of principle study suggests that if our findings are reproduced in larger samples, including abnormal white matter states, the technique may be useful in assessment of white matter diseases.

Full Text

Duke Authors

Cited Authors

  • Middleton, DM; Li, JY; Lee, HJ; Chen, S; Dickson, PI; Ellinwood, NM; White, LE; Provenzale, JM

Published Date

  • August 2017

Published In

Volume / Issue

  • 30 / 4

Start / End Page

  • 324 - 329

PubMed ID

  • 28631949

Pubmed Central ID

  • 28631949

Electronic International Standard Serial Number (EISSN)

  • 2385-1996

Digital Object Identifier (DOI)

  • 10.1177/1971400917709628

Language

  • eng

Conference Location

  • United States