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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Effects of rejecting diffusion directions on tensor-derived parameters.

Yiran Chen¹, Olga Tymofiyeva¹, Christopher P Hess¹

¹Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.

|January 14, 2015

Summary

Discarding Diffusion Tensor Imaging (DTI) data due to motion can alter results. Random or clustered data rejection significantly overestimates diffusion parameters like FA, especially in low FA regions.

Keywords:

Anisotropy Brain DTI MRI Motion

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Area of Science:

Neuroimaging
Medical Physics
Biomedical Engineering

Background:

Subject motion is a significant challenge in Diffusion Tensor Imaging (DTI), necessitating the removal of corrupted data.
The impact of discarding diffusion-weighted images on diffusion parameter estimation remains incompletely understood.

Purpose of the Study:

To investigate the consequences of excluding diffusion imaging volumes on key DTI parameters.
To analyze the effects of random, uniform, and clustered data rejection on fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), and the primary eigenvector (V1).

Main Methods:

Generated incomplete DTI datasets by excluding one or more volumes from a full Jones30 diffusion scheme acquisition.
Simulated three rejection strategies: random, uniform (evenly distributed directions), and clustered (grouped directions).
Quantified changes in FA, MD, AD, RD, and V1 across different rejection scenarios and numbers of excluded volumes.

Main Results:

Mean diffusivity (MD) showed minimal changes irrespective of the rejection strategy.
Random rejections led to overestimation of FA, AD, RD, and V1, with greater impact in low FA regions.
Clustered rejections resulted in the most substantial overestimation, highly dependent on the orientation of excluded directions relative to fiber structures.
Uniform rejections minimally affected FA and V1.

Conclusions:

The method and pattern of diffusion direction exclusion critically influence DTI parameter accuracy.
Awareness of the number and spatial distribution of rejected diffusion directions is crucial for precise DTI data analysis.
Clustered rejections pose the highest risk of parameter overestimation, particularly impacting analyses sensitive to fiber orientation.