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Related Experiment Video

Updated: May 12, 2026

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
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Differences of white matter structure for diffusion kurtosis imaging using voxel-based morphometry and connectivity

Yuki Kanazawa1, Natsuki Ikemitsu2, Yuki Kinjo3

  • 1Graduate School of Biomedical Sciences, Tokushima University, Tokushima 770-8503, Japan.

BJR Open
|February 14, 2024
PubMed
Summary

Diffusion kurtosis imaging (DKI) provides more detailed white matter (WM) information than diffusion tensor imaging (DTI). DKI analysis revealed clearer connectivity in WM structures, aiding in understanding neurological diseases.

Keywords:
diffusion kurtosisdiffusion tensor tractographymagnetic resonance imagingvoxel-based morphometrywhite matter

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Area of Science:

  • Neuroimaging
  • White Matter Analysis
  • Diffusion MRI

Background:

  • Diffusion kurtosis imaging (DKI) offers advanced visualization of white matter (WM) structures.
  • Comparing DKI and diffusion tensor imaging (DTI) parameters is crucial for understanding WM differences.

Purpose of the Study:

  • To evaluate and compare diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) parameter values.
  • To identify white matter (WM) structure differences in healthy subjects using DTI and DKI.

Main Methods:

  • Thirteen healthy volunteers underwent 3-T MRI with DKI and T1-weighted imaging.
  • Analysis involved registration to MNI152, applying DTI (FA, diffusivity) and DKI (MK, RK, AK) parameters, and tract-based spatial statistics (TBSS).

Main Results:

  • Strong correlations were found between fractional anisotropy (FA) and DKI parameters (MK, RK, AK).
  • Mean kurtosis (MK) provided clearer visualization of tract-based spatial statistics (TBSS) connections compared to radial kurtosis (RK) and FA.

Conclusions:

  • Diffusion kurtosis imaging (DKI) enhances white matter (WM) analysis, providing detailed information on nerve structure connectivity.
  • DKI facilitates quantitative assessment of neurological diseases through voxel-based morphometry of WM regions.