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Related Experiment Videos

Quantitative metrics for evaluating parallel acquisition techniques in diffusion tensor imaging at 3 Tesla.

Siamak Ardekani1, Luis Selva, James Sayre

  • 1Center for Cardiovascular Bioinformatics and Modeling, Johns Hopkins University, Maryland, USA.

Investigative Radiology
|October 13, 2006
PubMed
Summary
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Generalized auto-calibrating partially parallel acquisitions (GRAPPA) with an acceleration factor of 2 is optimal for diffusion tensor imaging, reducing distortions and noise effectively. This method offers superior performance compared to sensitivity encoding (SENSE) for diffusion tensor imaging quality.

Area of Science:

  • Magnetic Resonance Imaging
  • Diffusion Tensor Imaging
  • Medical Physics

Background:

  • Single-shot echo-planar based diffusion tensor imaging (DTI) suffers from geometric and intensity distortions.
  • Parallel imaging techniques can mitigate these distortions while maintaining spatial resolution.

Purpose of the Study:

  • To quantitatively compare parallel imaging methods, GRAPPA and SENSE, for DTI at 3 Tesla.
  • To evaluate their performance at various acceleration factors (R).

Main Methods:

  • Eight human subjects underwent DTI scans, with repeated scans for two subjects to assess reproducibility.
  • Mutual information (MI) measured global geometric distortions.
  • Regions of interest were used to evaluate noise and artifacts by analyzing the standard deviation of apparent diffusion coefficient (ADC) and fractional anisotropy (FA), and residual error.

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Main Results:

  • Increasing acceleration factors (R) reduced distortions, as indicated by higher MI values.
  • GRAPPA showed better distortion reduction than SENSE for a given R.
  • GRAPPA with R=2 demonstrated the least noise and artifacts in ADC and FA, with significant differences compared to SENSE R=2 for FA.

Conclusions:

  • GRAPPA with an acceleration factor of 2 is identified as the optimal parallel reconstruction method for diffusion tensor imaging.
  • This method balances distortion reduction, noise suppression, and image quality effectively.