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Diffusion tensor imaging-based tissue segmentation: validation and application to the developing child and adolescent

Khader M Hasan1, Christopher Halphen, Ambika Sankar

  • 1Department of Diagnostic and Interventional Imaging, University of Texas Medical School at Houston, 6431 Fannin Street, MSB 2.100 Houston, TX 77030, USA. Khader.M.Hasan@uth.tmc.edu

Neuroimage
|December 15, 2006
PubMed
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This study introduces a new diffusion tensor imaging (DTI) method to segment brain tissues like grey matter (GM) and white matter (WM). This DTI approach shows promise for analyzing both micro and macrostructural brain organization.

Area of Science:

  • Neuroimaging
  • Medical Physics
  • Radiology

Background:

  • Accurate segmentation of brain tissues is crucial for understanding neurological development and disease.
  • Conventional MRI methods provide valuable structural information but can be limited in differentiating certain tissue microstructures.
  • Diffusion Tensor Imaging (DTI) offers insights into white matter architecture and tissue properties.

Purpose of the Study:

  • To present and validate a novel Diffusion Tensor Imaging (DTI) approach for segmenting the human whole-brain into grey matter (GM), white matter (WM), and cerebrospinal fluid (CSF).
  • To compare DTI-derived brain tissue fractions with those obtained from conventional Magnetic Resonance Imaging (cMRI) methods.
  • To assess the utility of the DTI-based segmentation in characterizing age-related changes in brain development.

Related Experiment Videos

Main Methods:

  • The novel approach utilizes the contrast within DTI anisotropy and diffusivity rotational invariant space for tissue segmentation.
  • DTI-based whole-brain grey matter and white matter fractions (GMf and WMf) were calculated.
  • These DTI-derived fractions were contrasted with fractions from cMRI segmentation using T1w, PDw, and T2w contrasts.

Main Results:

  • The DTI-based segmentation demonstrated good correspondence with cMRI estimates of total brain volume.
  • The method accurately depicted expected age-related trends in white matter (WM) and grey matter (GM) volume percentages in developing brains (children aged 6-18 years).
  • The DTI approach successfully segmented GM, WM, and CSF, validating its efficacy.

Conclusions:

  • The novel DTI approach provides a robust method for whole-brain segmentation into GM, WM, and CSF.
  • This technique offers a valuable tool for assessing brain micro and macrostructural organization.
  • The DTI method shows potential for extending its application in developmental neuroscience and clinical research.