SNR phase order k-space encoding (SPOKE)

Magn Reson Imaging. 2007 Dec;25(10):1402-8. doi: 10.1016/j.mri.2007.03.033. Epub 2007 Jun 13.

Abstract

A method of determining the phase-encode order for MR Fourier-encoded imaging is described, which provides an additional option for optimizing images from samples with signals that change during data acquisition. Examples are in hyperpolarized helium gas imaging of the lungs where polarization is lost with each RF pulse or the signal changes observed in rapid dynamic studies with T(1) or T(2)* contrast agents when mixing is taking place. The method uses a single frequency-encoded projection in the proposed phase-encoding direction. The projection is subsequently sorted into signal-to-noise ratio (SNR) order. The indices of the sorted array are then used to create the phase-encode table to be used for the scan. This phase table is sorted in descending SNR order for signals that decrease during data acquisition and in ascending order for signals that increase during data acquisition. Simulations suggest that this technique can produce higher resolution than centric-ordered phase encoding at the expense of increased modulation (ghosting) artifact for dynamically changing signals. Initial practical implementation of the technique has been carried out on a dedicated 0.2-T Niche MR system, and the test object results agree well with simulations. Hyperpolarized 3-He lung images have also been acquired and postprocessed using the SNR phase order k-space encoding (SPOKE) methodology and show potential for improved imaging with high flip angles where polarization is rapidly lost. Applications may also be found for 3D volumetric acquisitions where two dimensions can be SPOKE encoded.

MeSH terms

  • Algorithms*
  • Artifacts*
  • Humans
  • Image Enhancement / methods*
  • Image Interpretation, Computer-Assisted / methods*
  • Information Storage and Retrieval / methods*
  • Lung / anatomy & histology*
  • Magnetic Resonance Imaging / methods*
  • Reproducibility of Results
  • Sensitivity and Specificity