Influence of macromolecule baseline on (1) H MR spectroscopic imaging reproducibility

PURPOSE: Poorly characterized macromolecular (MM) and baseline artefacts are known to reduce metabolite quantitation accuracy in (1)H MR spectroscopic imaging (MRSI). Increasing echo time (TE) and improvements in MM analysis schemes have both been proposed as strategies to improve metabolite measurement reliability. In this study, the influence of TE and two MM analysis schemes on MRSI reproducibility are investigated. METHODS: An experimentally acquired baseline was collected using an inversion recovery sequence (TI = 750 ms) and incorporated into the analysis method. Intrasubject reproducibility of MRSI scans, acquired at 3 Tesla, was assessed using metabolite coefficients of variance (COVs) for both experimentally acquired and simulated MM analysis schemes. In addition, the reproducibility of TE = 35 ms, 80 ms, and 144 ms was evaluated. RESULTS: TE = 80 ms was the most reproducible for singlet metabolites with COVs < 6% for total N‐acetyl‐aspartate, total creatine, and total choline; however, moderate multiplet dephasing was observed. Analysis incorporating the experimental baseline achieved higher Glu and Glx reproducibility at TE = 35 ms, and showed improvements over the simulated baseline, with higher efficacy for poorer data. CONCLUSION: Overall, TE = 80 ms yielded the most reproducible singlet metabolite estimates. However, combined use of a short TE sequence and the experimental baseline may be preferred as a compromise between accuracy, multiplet dephasing, and T2 bias on metabolite estimates. Magn Reson Med 77:34–43, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine

Developments in magnetic resonance spectroscopic imaging acquisition and analysis

Magnetic Resonance Spectroscopic Imaging (MRSI), a functional MR imaging technique, has proven via the identification of metabolite biomarkers to be useful in the diagnosis and prognosis of numerous diseases, for example brain tumours. However, a number of factors impede its routine clinical use: i) long acquisition times mean its use is limited to low resolution 2-dimensional slabs, ii) large quantity of data produced means its interpretation can be time consuming and iii) data quality can be variable and therefore interpretation can be difficult for a non-expert. Further developments in MRSI are designed to reduce the impact of these issues. The focus of this work is to address some of the above issues; developing acquisition protocols and optimising analysis methods in order to increase the clinical feasibility of MRSI. Within this study a fast-MRSI protocol has been developed for absolute metabolite quantitation and has demonstrated its feasibility for clinical use, accurately reproducing data in a shorter clinically feasible acquisition time. An experimentally derived fitting model has been developed which increases metabolite measurement accuracy. Finally, a 3D MRSI protocol has been successfully optimized allowing robust metabolite information to be mapped throughout the brain