Least-squares chemical shift separation for (13)C metabolic imaging

Area of Science:

• Magnetic Resonance Imaging
• Metabolic Imaging
• Spectroscopy

Background:

• Sparse spectra in metabolic imaging present challenges for separating closely related chemical species.
• Accurate metabolite quantification is crucial for understanding metabolic pathways.
• Hyperpolarized (13)C imaging offers insights into metabolic dynamics but requires efficient data acquisition.

Purpose of the Study:

• To introduce a novel least-squares chemical shift imaging (LSCSI) method for separating chemical species with widely spaced peaks in sparse spectra.
• To demonstrate the capability of LSCSI to account for chemical species exhibiting multiple peaks.
• To optimize data acquisition for efficient metabolite separation.

Main Methods:

• The LSCSI method was applied to imaging of (13)C-labeled pyruvate and its metabolites (alanine, pyruvate, lactate).
• It leverages a priori knowledge of resonant frequencies and relative signal intensities of different chemical species.
• A least-squares approach was employed for signal separation, significantly reducing data requirements.
• Echo spacing was optimized for maximal noise performance in signal separation.

Main Results:

• LSCSI demonstrated excellent metabolite separation in a (13)C phantom at 3.0T, comparable to echo planar spectroscopic imaging (EPSI).
• The method achieved this high-fidelity separation using only 1/16th of the data required by EPSI.
• Significant reductions in data acquisition were facilitated by the least-squares decomposition of chemical species.

Conclusions:

• The LSCSI method offers a significant advantage for in vivo hyperpolarized (13)C metabolic applications.
• Its ability to reduce scan time compared to EPSI makes it highly beneficial for clinical translation.
• This approach enhances the feasibility of rapid and accurate metabolic assessment using magnetic resonance.