Advanced magnetic resonance spectroscopic neuroimaging: Experts' consensus recommendations

Abstract

Magnetic resonance spectroscopic imaging (MRSI) offers considerable promise for monitoring metabolic alterations associated with disease or injury; however, to date, these methods have not had a significant impact on clinical care, and their use remains largely confined to the research community and a limited number of clinical sites. The MRSI methods currently implemented on clinical MRI instruments have remained essentially unchanged for two decades, with only incremental improvements in sequence implementation. During this time, a number of technological developments have taken place that have already greatly benefited the quality of MRSI measurements within the research community and which promise to bring advanced MRSI studies to the point where the technique becomes a true imaging modality, while making the traditional review of individual spectra a secondary requirement. Furthermore, the increasing use of biomedical MR spectroscopy studies has indicated clinical areas where advanced MRSI methods can provide valuable information for clinical care. In light of this rapidly changing technological environment and growing understanding of the value of MRSI studies for biomedical studies, this article presents a consensus from a group of experts in the field that reviews the state-of-the-art for clinical proton MRSI studies of the human brain, recommends minimal standards for further development of vendor-provided MRSI implementations, and identifies areas which need further technical development.

High-resolution metabolic imaging of high-grade gliomas using 7T-CRT-FID-MRSI

“…In order to reliably image gliomas in the basal parts of the brain, significant improvements in B 0 shim hardware is warranted to improve B 0 homogeneity throughout the brain ( Juchem et al, 2020 , Wilson et al, 2019 ). Together with motion correction, this will improve the robustness of 7T MRSI, particularly when whole-brain coverage is required ( Maudsley et al, 2020 ).…”

Preprocessing, analysis and quantification in single‐voxel magnetic resonance spectroscopy: experts' consensus recommendations

“…Quantitatively, this can be expressed using the fit quality number (FQN), which is the ratio of the variance in the fit residual divided by the variance in the pure spectral noise. 62 For an ideal fit, the FQN should be close to 1.0, and the FQN/SNR ratio should be much less than 1. Some examples of linear combination model fitting are shown in Figure 5.…”

PCA denoising and Wiener deconvolution of 31P 3D CSI data to enhance effective SNR and improve point spread function

“…This is most likely due to the increased linewidth of the PCr peak because we used the maximal absolute signal intensity for the SNR calculations. The effect of processing of fitting performance depends on the method used 27,58 and whether the fitting is done in the time or frequency domain or a combination of both 59 . However, for signal fitting a higher effective SNR is generally beneficial 27,58 …”