Purpose: To describe a new least-squares chemical shift (LSCSI) method for separation of chemical species with widely spaced peaks in a sparse spectrum. The ability to account for species with multiple peaks is addressed.
Materials and methods: This method is applied to imaging of (13)C-labeled pyruvate and its metabolites alanine, pyruvate, and lactate. The method relies on a priori knowledge of the resonant frequencies of the different chemical species, as well as the relative signal from the two pyruvate peaks, one of which lies near the alanine peak. With this information a least-squares method was utilized for separation of signal from the three metabolites, facilitating tremendous reductions in the amount of data required to decompose the different chemical species. Optimization of echo spacing for maximum noise performance of the signal separation is also described.
Results: Imaging an enriched (13)C phantom at 3.0T, the LSCSI method demonstrates excellent metabolite separation, very similar to echo planar spectroscopic imaging (EPSI), while only using 1/16th as much data.
Conclusion: This approach may be advantageous for in vivo hyperpolarized (13)C metabolic applications for reduced scan time compared with EPSI.
(c) 2007 Wiley-Liss, Inc.