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Atlas-based fiber bundle segmentation using principal diffusion directions and spherical harmonic coefficients.

Mohammad-Reza Nazem-Zadeh1, Esmaeil Davoodi-Bojd, Hamid Soltanian-Zadeh

  • 1Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran 14395-515, Iran. m.nazemzadeh@ece.ut.ac.ir

Neuroimage
|September 28, 2010
PubMed
Summary

This study presents an automated atlas-based method for segmenting fiber bundles in High Angular Resolution Diffusion Imaging (HARDI) data. The novel approach improves accuracy in complex fiber crossing areas without manual seed selection.

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Area of Science:

  • Neuroimaging
  • Diffusion MRI
  • Computational Neuroscience

Background:

  • Current fiber bundle segmentation methods in High Angular Resolution Diffusion Imaging (HARDI) lack accuracy and are subjective due to manual seed selection.
  • Existing techniques often rely on similarity measures that are insufficient for precise segmentation, especially in areas with complex fiber crossings.

Purpose of the Study:

  • To develop an automated atlas-based method for segmenting white matter fiber bundles using HARDI data.
  • To overcome limitations of previous methods by eliminating subjective operator-defined seeds and Regions of Interest (ROIs).

Main Methods:

  • Utilizes Spherical Harmonic Coefficients (SHC) from HARDI signals to compute Orientation Distribution Functions (ODFs) and extract Principal Diffusion Directions (PDDs).
  • Employs an atlas-based approach for automated seed selection and a novel similarity measure within a level set framework for segmentation.
  • Validates the method using simulated data with crossing fiber patterns and real HARDI data from normal controls.

Main Results:

  • Demonstrates successful segmentation of various fiber bundles in both simulated and real HARDI data.
  • Evaluates the impact of threshold selection on segmentation accuracy using the Dice coefficient.
  • Assesses the effect of increasing gradient directions on the accuracy of extracted PDDs.

Conclusions:

  • The proposed automated atlas-based method offers superior performance compared to traditional techniques, particularly those based on Diffusion Tensor Imaging (DTI).
  • Achieves accurate segmentation in fiber crossing regions by effectively propagating a hyper-surface without assumptions on diffusivity or initial seed placement.