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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
Published on: July 28, 2013
Comparison of generalized autocalibrating partially parallel acquisitions and modified sensitivity encoding for
Y A Bhagat1, D J Emery, S Naik
1Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada.
AJNR. American Journal of Neuroradiology
|February 14, 2007
Summary
Parallel imaging techniques like GRAPPA and mSENSE significantly reduce artifacts in brain diffusion tensor imaging (DTI). Acceleration factor 2 (R=2) yielded high-quality images and reproducible quantitative diffusion measurements.
Area of Science:
- Neuroimaging
- Magnetic Resonance Imaging (MRI)
- Diffusion Tensor Imaging (DTI)
Background:
- Conventional DTI brain imaging uses single-shot echo-planar imaging, prone to signal loss, geometric distortions, and blurring.
- Parallel imaging techniques shorten echo-train acquisition, potentially reducing these artifacts.
Purpose of the Study:
- Evaluate self-calibrating parallel acquisition techniques: modified sensitivity encoding (mSENSE) and generalized autocalibrating partially parallel acquisitions (GRAPPA).
- Compare these techniques against conventional DTI in healthy subjects.
Main Methods:
- GRAPPA and mSENSE with acceleration factors (R) up to 4 were compared to conventional DTI.
- Image quality was assessed qualitatively (sharpness, artifacts) and quantitatively (SNR, ADC, FA) in white and gray matter regions.
Main Results:
- Reviewers preferred GRAPPA and mSENSE at R=2 over conventional DTI.
- Improved fractional anisotropy (FA) contrast was observed at the gray/white matter junction.
- While quantitative diffusion measurements (ADC, FA) were consistent, higher acceleration factors (R=3,4) introduced reconstruction artifacts.
Conclusions:
- GRAPPA and mSENSE with R=2 effectively minimize susceptibility and off-resonance artifacts in DTI.
- These parallel imaging methods produce high-quality brain images and reliable quantitative diffusion data.
