Dataset Information

On the Utility of Short Echo Time (TE) Single Voxel 1H-MRS in Non-Invasive Detection of 2-Hydroxyglutarate (2HG); Challenges and Potential Improvement Illustrated with Animal Models Using MRUI and LCModel.

ABSTRACT: Mutations in isocitrate dehydrogenase 1 and 2 (IDH1/2) are frequently found in brain tumors, and the resulting onco-metabolite, 2-hydroxyglutarate (2HG), has been suggested to be a potential diagnostic and prognostic biomarker of the diseases. Indeed, recent studies have demonstrated the feasibility of non-invasively detecting 2HG by using proton magnetic resonance spectroscopy (1H-MRS). Due to severe spectral overlaps of 2HG with its background metabolites and spectral baselines, however, the majority of those previous studies employed spectral editing methods with long echo times (TEs) instead of the most commonly used short TE approach with spectral fitting. Consequently, the results obtained with spectral editing methods may potentially be prone to errors resulting from substantial signal loss due to relaxation. Given that the spectral region where the main signal of 2HG resides is particularly sensitive to spectral baseline in metabolite quantification, we have investigated the impact of incorporating voxel-specifically measured baselines into the spectral basis set on the performance of the conventional short TE approach in 2HG detection in rodent models (Fisher 344 rats; n = 19) of IDH1/2 mutant-overexpressing F98 glioma at 9.4T. Metabolite spectra were acquired (SPECIAL sequence) for a tumor region and the contralateral normal region of the brain for each animal. For the estimation of spectral baselines metabolite-nulled spectra were obtained (double-inversion-recovery SPECIAL sequence) for each individual voxels. Data were post-processed with and without the measured baselines using MRUI and LCModel-the two most widely used data post-processing packages. Our results demonstrate that in-vivo detection of 2HG using the conventional short TE approach is challenging even at 9.4T. However, incorporation of voxel-specifically measured spectral baselines may potentially improve its performance. Upon more thorough validation in a larger number of animals and more importantly in human patients, the potential utility of the proposed short TE acquisition with voxel-specific baseline measurement approach in 2HG detection may need to be considered in the study design.

SUBMITTER: Heo H

PROVIDER: S-EPMC4731570 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

On the Utility of Short Echo Time (TE) Single Voxel 1H-MRS in Non-Invasive Detection of 2-Hydroxyglutarate (2HG); Challenges and Potential Improvement Illustrated with Animal Models Using MRUI and LCModel.

Heo Hwon H Kim Sungjin S Lee Hyeong Hun HH Cho Hye Rim HR Xu Wen Jun WJ Lee Se-Hoon SH Park Chul-Kee CK Park Sunghyouk S Choi Seung Hong SH Kim Hyeonjin H

PloS one 20160128 1

Mutations in isocitrate dehydrogenase 1 and 2 (IDH1/2) are frequently found in brain tumors, and the resulting onco-metabolite, 2-hydroxyglutarate (2HG), has been suggested to be a potential diagnostic and prognostic biomarker of the diseases. Indeed, recent studies have demonstrated the feasibility of non-invasively detecting 2HG by using proton magnetic resonance spectroscopy (1H-MRS). Due to severe spectral overlaps of 2HG with its background metabolites and spectral baselines, however, the m ...[more]

PMID: 26820720

Similar Datasets

Project description:Out-of-voxel (OOV) signals are common spurious echo artifacts in MRS. These signals often manifest in the spectrum as very strong "ripples," which interfere with spectral quantification by overlapping with targeted metabolite resonances. Dephasing optimization through coherence order pathway selection (DOTCOPS) gradient schemes are algorithmically optimized to suppress all potential alternative coherence transfer pathways (CTPs), and should suppress unwanted OOV echoes. In addition, second-order shimming uses non-linear gradient fields to maximize field homogeneity inside the voxel, which unfortunately increases the diversity of local gradient fields outside of the voxel. Given that strong local spatial B0 gradients can refocus unintended CTPs, it is possible that OOVs are less prevalent when only linear first-order shimming is applied. Here we compare the size of unwanted OOV signals in Hadamard-edited (HERMES) data acquired with either a local gradient scheme (which we refer to here as "Shared") or DOTCOPS, and with first- or second-order shimming. We collected data from 15 healthy volunteers in two brain regions (voxel size 30 × 26 × 26 mm3 ) from which it is challenging to acquire MRS data: medial prefrontal cortex and left temporal cortex. Characteristic OOV echoes were seen in both GABA- and GSH-edited spectra for both brain regions, gradient schemes, and shimming approaches. A linear mixed-effect model revealed a statistically significant difference in the average residual based on the gradient scheme in both GABA- (p < 0.001) and GSH-edited (p < 0.001) spectra: that is, the DOTCOPS gradient scheme resulted in smaller OOV artifacts compared with the Shared scheme. There were no significant differences in OOV artifacts associated with shimming method. Thus, these results suggest that the DOTCOPS gradient scheme for J-difference-edited PRESS acquisitions yields spectra with smaller OOV echo artifacts than the Shared gradient scheme implemented in a widely disseminated editing sequence.

Project description:BackgroundMagnetic Resonance Spectroscopy (MRS) can measure in vivo brain tissue metabolism that exhibits unique biochemical characteristics in brain tumors. For clinical application, an efficient and versatile quantification method of MRS would be an important tool for medical research, particularly for exploring the scientific problem of tumor monitoring. The objective of our study is to propose an automated MRS quantitative approach and assess the feasibility of this approach for glioma grading, prognosis and boundary detection.MethodsAn automated quantitative approach based on a convex envelope (AQoCE) is proposed in this paper, including preprocessing, convex-envelope based baseline fitting, bias correction, sectional baseline removal, and peak detection, in a total of 5 steps. Some metabolic ratios acquired by this quantification are selected for statistical analysis. An independent sample t-test and the Kruskal-Wallis test are used for distinguishing low-grade gliomas (LGG) and high-grade gliomas (HGG) and for detecting the tumor, peritumoral and contralateral areas, respectively. Seventy-eight cases of pre-operative brain gliomas with pathological reports are included in this study.ResultsCho/NAA, Cho/Cr and Lip-Lac/Cr (LL/Cr) calculated by AQoCE in the tumor area differ significantly between LGG and HGG, with p≤0.005. Using logistic regression combining Cho/NAA, Cho/Cr and LL/Cr to generate a ROC curve, AQoCE achieves a sensitivity of 92.9%, a specificity of 72.2%, and an area under ROC curve (AUC) of 0.860. Moreover, both Cho/NAA and Cho/Cr in the AQoCE approach show a significant difference (p≤0.019) between tumoral, peritumoral, and contralateral areas. The comparison between the results of AQoCE and Siemens MRS processing software are also discussed in this paper.ConclusionsThe AQoCE approach is an automated method of residual water removal and metabolite quantification. It can be applied to multi-voxel 1H-MRS for evaluating brain glioma grading and demonstrating characteristics of brain glioma metabolism. It can also detect infiltration in the peritumoral area. Under the limited clinical data used, AQoCE is significantly more versatile and efficient compared to the reference approach of Siemens.

Dataset Information

On the Utility of Short Echo Time (TE) Single Voxel 1H-MRS in Non-Invasive Detection of 2-Hydroxyglutarate (2HG); Challenges and Potential Improvement Illustrated with Animal Models Using MRUI and LCModel.

Publications

On the Utility of Short Echo Time (TE) Single Voxel 1H-MRS in Non-Invasive Detection of 2-Hydroxyglutarate (2HG); Challenges and Potential Improvement Illustrated with Animal Models Using MRUI and LCModel.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets