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Related Experiment Videos

Critical sampling in ROSE scanning.

G E Sarty1

  • 1Department of Medical Imaging, University of Saskatchewan, Saskatoon, Saskatchewan, Canada. sarty@maya.usask.ca

Magnetic Resonance in Medicine
|July 14, 2000
PubMed
Summary

To prevent aliasing in Radially Oriented Sinusoidal Excursions (ROSE) imaging, carefully select gradient frequency, interleaves, and samples. Proper ROSE parameter selection ensures accurate k-space data acquisition for clear image reconstruction.

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Area of Science:

  • Magnetic Resonance Imaging (MRI)
  • Image Reconstruction
  • k-space Data Acquisition

Background:

  • Radially Oriented Sinusoidal Excursions (ROSE) is a k-space trajectory used in MRI.
  • Acquisition parameters like gradient frequency and interleaves critically affect ROSE data quality.
  • Aliasing artifacts can compromise the diagnostic value of reconstructed MRI images.

Purpose of the Study:

  • To define the optimal acquisition parameters for ROSE data to eliminate aliasing.
  • To establish criteria for achieving artifact-free image reconstruction using ROSE sequences.

Main Methods:

  • Analysis of k-space data distribution for uniformly sampled ROSE acquisitions.
  • Mathematical determination of aliasing elimination conditions based on ROSE parameters.
  • Evaluation of the relationship between ROSE petals, sample circles, and image dimensions.

Main Results:

  • Uniform temporal sampling in ROSE results in irregularly spaced circles in k-space.
  • Aliasing is eliminated when the number of sample circles matches the image dimension's pixel count.
  • Azimuthal aliasing is fully resolved with four times the pixels in ROSE petals, though fewer may suffice.

Conclusions:

  • Appropriate selection of gradient frequency, interleaves, and samples is essential for unaliased ROSE data acquisition.
  • Specific relationships between ROSE parameters and image dimensions are key to preventing aliasing artifacts.
  • These findings provide guidelines for optimizing ROSE sequences for high-fidelity MRI reconstruction.

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