
PurposeA “relaxation‐enhanced” (RE) approach to acquire in vivo localized spectra with flat baselines and good sensitivity has been recently proposed. As RE MR spectroscopy (MRS) targets a subset of a priori known resonances, new possibilities arise to acquire spectroscopic imaging data in faster, more efficient manners. This is hereby illustrated by Spectroscopically Encoded Chemical Shift Imaging (SECSI).MethodsSECSI delivers spectral/spatial correlations by collecting gradient echo trains whose timings are defined by the shifts of the resonances to be disentangled. Condition number considerations allow one to unravel these image contributions for various sites by a simple matrix inversion. The efficiency of the ensuing method is high enough to enable a sampling of additional spatial axes by means of their phase encoding in spin‐echo trains.ResultsThe one‐dimensional (1D) spectral / 2D spatial SECSI acquisitions were implemented on phantom, ex vivo, and in vivo models. In all cases, quality site‐resolved images were obtained. The experimentally observed enhancements were consistent with theoretical signal‐to‐noise ratio derivations.ConclusionWhile still bound by MRSI's sensitivity limitations, a novel spectroscopic imaging protocol exploiting a priori information, selective excitations and multiple echo encodings, was proposed and demonstrated. The method is promising when dealing with high T2/ ratios, sparse data, or hyperpolarization studies. Magn Reson Med 77:511–519, 2017. © 2016 International Society for Magnetic Resonance in Medicine
Brain Chemistry, Magnetic Resonance Spectroscopy, Phantoms, Imaging, Reproducibility of Results, Signal Processing, Computer-Assisted, Magnetic Resonance Imaging, Sensitivity and Specificity, Molecular Imaging, Mice, Animals, Humans, Algorithms
Brain Chemistry, Magnetic Resonance Spectroscopy, Phantoms, Imaging, Reproducibility of Results, Signal Processing, Computer-Assisted, Magnetic Resonance Imaging, Sensitivity and Specificity, Molecular Imaging, Mice, Animals, Humans, Algorithms
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