Multi-echo single-shot spectroscopy combined with simultaneous 2D model fitting for fast and accurate measurement of metabolite-specific concentrations and T 2 relaxation times.

The purpose of the current study was to develop a novel single-voxel MR spectroscopy acquisition scheme to simultaneously determine metabolite-specific concentrations and transverse relaxation times within realistic clinical scan times. Partly truncated multi-TE data are acquired as an echo train in a single acquisition (multi-echo single-shot [MESS]). A 2D multiparametric model fitting approach combines truncated, low-resolved short TE data with fully sampled, highly resolved, longer TE data to yield concentration and T 2 estimates for major brain metabolites simultaneously. Cramer-Rao lower bounds (CRLB) are used as a measure of performance. The novel scheme was compared with traditional multi-echo multi-shot methods. In silico, in vitro, and in vivo experiments support the findings. MESS schemes, requiring only 2 min 12 s for the acquisition of three echo times, provide valid concentration and relaxation estimates for multiple metabolites and outperform traditional methods for simultaneous determinations of metabolite-specific T 2 s and concentrations, with improvements ranging from 5% to 30% for T 2 s and from 10% to 50% for concentrations. However, substantial unsuppressed residual water signals may hamper the method's reproducibility, as observed in an initial experiment setup that prioritizes short TEs with severely truncated acquisition for the benefit of signal-to-noise ratio (SNR). Nevertheless, CRLB have been confirmed to be well suited as design criteria, and within-session repeatability approaches CRLB when residual water is removed in postprocessing by exploiting longer and less truncated data recordings. MESS MRS combined with 2D model fitting promises comparable accuracy, increased precision, or inversely shorter experimental times compared with traditional approaches. However, the optimal design must be investigated as a trade-off between SNR, the truncation factor, and TE batch selections, all of which influence the robustness of estimations.