Accelerated short-TE 3D proton echo-planar spectroscopic imaging using 2D-SENSE with a 32-channel array coil

Ricardo Otazo*, Shang Yueh Tsai, Fa Hsuan Lin, Stefan Posse

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

37 Scopus citations

Abstract

MR spectroscopic imaging (MRSI) with whole brain coverage in clinically feasible acquisition times still remains a major challenge. A combination of MRSI with parallel imaging has shown promise to reduce the long encoding times and 2D acceleration with a large array coil is expected to provide high acceleration capability. In this work a very high-speed method for 3D-MRSI based on the combination of proton echo planar spectroscopic imaging (PEPSI) with regularized 2D-SENSE reconstruction is developed. Regularization was performed by constraining the singular value decomposition of the encoding matrix to reduce the effect of low-value and overlapped coil sensitivities. The effects of spectral heterogeneity and discontinuities in coil sensitivity across the spectroscopic voxels were minimized by unaliasing the point spread function. As a result the contamination from extracranial lipids was reduced 1.6-fold on average compared to standard SENSE. We show that the acquisition of short-TE (15 ms) 3D-PEPSI at 3 T with a 32 x 32 x 8 spatial matrix using a 32-channel array coil can be accelerated 8-fold (R = 4 x 2) along y-z to achieve a minimum acquisition time of 1 min. Maps of the concentrations of N-acetyl-aspartate, creatine, choline, and glutamate were obtained with moderate reduction in spatial-spectral quality. The short acquisition time makes the method suitable for volumetric metabolite mapping in clinical studies.

Original languageEnglish
Pages (from-to)1107-1116
Number of pages10
JournalMagnetic Resonance in Medicine
Volume58
Issue number6
DOIs
StatePublished - 12 2007
Externally publishedYes

Keywords

  • Echo-planar spectroscopic imaging
  • Large array coil
  • Parallel imaging
  • Regularization
  • SENSE

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