Refined modelling of the short-T₂signal component and ensuing detection of glutamate and glutamine in short-TE, localised,¹H MR spectra of human glioma measured at 3 T

Abstract

Short-TE (1) H MRS has great potential for brain cancer diagnostics. A major difficulty in the analysis of the spectra is the contribution from short-T2 signal components, mainly coming from mobile lipids. This complicates the accurate estimation of the spectral parameters of the resonance lines from metabolites, so that a qualitative to semi-quantitative interpretation of the spectra dominates in practice. One solution to overcome this difficulty is to measure and estimate the short-T2 signal component and to subtract it from the total signal, thus leaving only the metabolite signals. The technique works well when applied to spectra obtained from healthy individuals, but requires some optimisation during data acquisition. In the clinical setting, time constraints hardly allow this. Here, we propose an iterative estimation of the short-T2 signal component, acquired in a single acquisition after measurement of the full spectrum. The method is based on QUEST (quantitation based on quantum estimation) and allows the refinement of the estimate of the short-T2 signal component after measurement. Thus, acquisition protocols used on healthy volunteers can also be used on patients without further optimisation. The aim is to improve metabolite detection and, ultimately, to enable the estimation of the glutamine and glutamate signals distinctly. These two metabolites are of great interest in the characterisation of brain cancer, gliomas in particular. When applied to spectra from healthy volunteers, the new algorithm yields similar results to QUEST and direct subtraction of the short-T2 signal component. With patients, up to 12 metabolites and, at least, seven can be quantified in each individual brain tumour spectrum, depending on the metabolic state of the tumour. The refinement of the short-T2 signal component significantly improves the fitting procedure and produces a separate short-T2 signal component that can be used for the analysis of mobile lipid resonances. Thus, in brain tumour spectra, distinct estimates of signals from glutamate and glutamine are possible. Copyright © 2016 John Wiley & Sons, Ltd.

Improved resolution of glutamate, glutamine and γ‐aminobutyric acid with optimized point‐resolved spectroscopy sequence timings for their simultaneous quantification at 9.4 T

“…The objective of this work is to optimize TE 1 and TE 2 of the readily available PRESS sequence, which is commonly employed in in vivo MRS and potentially offers twice the SNR obtainable with STEAM (also commonly employed) to simultaneously quantify Glu, Gln and GABA at 9.4 T. Numerical calculations are performed to evaluate the J ‐coupling evolution of Glu, Gln, GABA and NAA to find a long TE that minimizes the undesired NAA signal at ≈2.49 ppm, whilst retaining signal from Gln at ≈2.45 ppm, Glu at ≈2.35 ppm and GABA at ≈2.28 ppm. The longer TE value also enables the suppression of MM signals, which contaminate the Glu, Gln and GABA signals at short TEs . The efficacy of the timings is verified on phantom solutions and on rat brain in vivo .…”

“…The longer TE value also enables the suppression of MM signals, which contaminate the Glu, Gln and GABA signals at short TEs. [54][55][56] The efficacy of the timings is verified on phantom solutions and on rat brain in vivo. Furthermore, LCModel quantification is assessed on spectra obtained from phantoms of known concentrations with both short-TE PRESS and the optimal-TE combination.…”

A Comparison of 7 Tesla MR Spectroscopic Imaging and 3 Tesla MR Fingerprinting for Tumor Localization in Glioma Patients

“…Compared to ratios, concentration estimates (CEs) offer more reliability and should be explored in future work [ 35 ]. Unfortunately, they come with their own challenges; in the case of internal water referencing, one needs to derive a water concentration in tumor tissue, which can be difficult in practice [ 36 , 37 , 38 ].…”

Macromolecule mapping of the brain using ultrashort‐TE acquisition and reference‐based metabolite removal

“…This approach does not rely on special acquisitions but can produce large estimation variations especially for multi‐voxel data, due to the sensitivity to noise and model mismatch for the nonlinear extrapolation problem. A combination of both approaches have also been used for improved quantification of short‐TE MR spectroscopy or MR spectroscopic imaging (MRSI) data, which typically require two scans for effective separation of MM and metabolite signals .…”