Age-related differences in macromolecular resonances observed in ultra-short-TE STEAM MR spectra at 7T

PURPOSE

To understand how macromolecular content varies in the human brain with age in a large cohort of healthy subjects.

METHODS

In-vivo 1H-MR spectra were acquired using ultra-short TE STEAM at 7T in the posterior cingulate cortex. Macromolecular content was studied in 147 datasets from a cohort ranging in age from 19 to 89 y. Three fitting approaches were used to evaluate the macromolecular content: (1) a macromolecular resonances model developed for this study; (2) LCModel-simulated macromolecules; and (3) a combination of measured and LCModel-simulated macromolecules. The effect of age on the macromolecular content was investigated by considering age both as a continuous variable (i.e., linear regressions) and as a categorical variable (i.e., multiple comparisons among sub-groups obtained by stratifying data according to age by decade).

RESULTS

While weak age-related effects were observed for macromolecular peaks at ˜0.9 (MM09), ˜1.2 (MM12), and ˜1.4 (MM14) ppm, moderate to strong effects were observed for peaks at ˜1.7 (MM17), and ˜2.0 (MM20) ppm. Significantly higher MM17 and MM20 content started from 30 to 40 y of age, while for MM09, MM12, and MM14, significantly higher content started from 60 to 70 y of age.

CONCLUSIONS

Our findings provide insights into age-related differences in macromolecular contents and strengthen the necessity of using age-matched measured macromolecules during quantification.

The effects of residual dipolar coupling on carnosine in proton muscle spectra

Carnosine, an MR-visible dipeptide in human muscle, is well characterized by two peaks at ~8 and ~7 ppm from C2 and C4 imidazole protons. Like creatine and other metabolites, carnosine is subject to residual dipolar coupling in the anisotropic environment of muscle fibers, but the effects have not been studied extensively. Single-voxel TE 30-32 PRESS spectra from three different 3T studies were acquired from gastrocnemius medialis and soleus muscles in the human lower leg. In these studies, carnosine T2 values were measured, and spectra were obtained at three different foot angles. LCModel was used to fit the carnosine peaks with a basis set that was generated using shaped RF pulses and included a range of dipolar couplings affecting the C4 peak. A seven-parameter analytic expression was used to fit the CH2 doublets of creatine. It incorporated an optimized "effective TE" value to model the effect of shaped RF pulses. The fits confirm that the triplet C4 peak of carnosine is dipolar coupled to a pair of CH2 protons, with no need to include a contribution from a separate pool of freely rotating uncoupled carnosine. Moreover, the couplings experienced by carnosine C4 protons and creatine CH2 protons are strongly correlated (R2  =  0.88, P<0.001), exhibiting a similar 3cos2 θ - 1 dependence on the angle θ between fiber orientation and B0. T2 values for the singlet C2 peak of gastrocnemius carnosine are inversely proportional to the C4 dipolar coupling strength (R2  = 0.97, P < 0.001), which in turn is a function of foot orientation. This dependence indicates that careful positioning of the foot while acquiring lower leg muscle spectra is important to obtain reproducible carnosine concentrations. As proton magnetic resonance spectroscopy of carnosine is currently used to non-invasively estimate the muscle fiber typology, these results have important implications in sport science.

Quantification of Cerebral Glucose Concentrations via Detection of the H1-α-Glucose Peak in 1 H MRS at 7 T

BACKGROUND

Sensitive detection and quantification of cerebral glucose is desired.

PURPOSE

To quantify cerebral glucose by detecting the H1-α-glucose peak at 5.23 ppm in 1 H magnetic resonance spectroscopy at 7 T.

STUDY TYPE

Prospective.

SUBJECTS

Twenty-eight non-fasted healthy subjects (aged 20-28 years).

FIELD STRENGTH/SEQUENCE

Short echo time stimulated echo acquisition mode (short-TE STEAM) and semi-localized by adiabatic selective refocusing (semi-LASER) at 7 T.

ASSESSMENT

Single voxel spectra were obtained from the posterior cingulate cortex (27-mL) using a 32-channel head coil. The H1-α-glucose peak in the spectrum with retrospective removal of the residual water peak was fitted using LCModel with a glucose basis set of only the H1-α-glucose peak. Conventional spectral analysis was performed with a glucose basis set of a full spectral pattern of glucose, also. Fitting precision was evaluated with Cramér-Rao lower bounds (CRLBs). The repeatability of glucose quantification via the semi-LASER sequence was tested.

STATISTICAL TESTS

Paired or Welch's t-test were used for normally distributed values. A P value of <0.05 was considered significant. The repeatability of measures was analyzed using coefficient of variation (CV).

RESULTS

Removal of the residual water peak improved the flatness and stability of baselines around the H1-α-glucose peak and reduced CRLBs for fitting the H1-α-glucose peak. The semi-LASER sequence was superior to the short-TE STEAM in the higher signal-to-noise ratio of the H1-α-glucose peak (mean ± SD 7.9 ± 2.5, P < 0.001). The conventional analysis overfitted the H1-α-glucose peak. The individual CVs of glucose quantification by detecting the H1-α-glucose peak were smaller than the corresponding CRLBs.

DATA CONCLUSION

Cerebral glucose concentration is quantitated to be 1.07 mM by detecting the H1-α-glucose peak in the semi-LASER spectra. Despite requiring long scan times, detecting the H1-α-glucose peak allows true glucose quantification free from the influence of overlapping taurine and macromolecule signals.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY STAGE: 1.

Improved reproducibility of γ-aminobutyric acid measurement from short-echo-time proton MR spectroscopy by linewidth-matched basis sets in LCModel

γ-Aminobutyric acid (GABA), as the primary inhibitory neurotransmitter, is extremely important for maintaining healthy brain function, and deviations from GABA homeostasis are related to various brain diseases. Short-echo-time (short-TE) proton MR spectroscopy (1 H-MRS) has been employed to measure GABA concentration from various human brain regions at high magnetic fields. The aim of this study was to investigate the effect of spectral linewidth on GABA quantification and explore the application of an optimized basis-set preparation approach using a spectral-linewidth-matched (LM) basis set in LCModel to improve the reproducibility of GABA quantification from short-TE 1 H-MRS. In contrast to the fixed-linewidth basis-set approach, the LM basis-set preparation approach, where all metabolite basis spectra were simulated with a linewidth 4 Hz narrower than that of water, showed a smaller standard deviation of estimated GABA concentration from synthetic spectra with varying linewidths and lineshapes. The test-retest reproducibility was assessed by the mean within-subject coefficient of variation, which improved from 19.2% to 12.0% in the thalamus, from 27.9% to 14.9% in the motor cortex, and from 9.7% to 2.8% in the medial prefrontal cortex using LM basis sets at 7 T. We conclude that spectral linewidth has a large effect on GABA quantification from short-TE 1 H-MRS data and that using LM basis sets in LCModel can improve the reproducibility of GABA quantification.

Spatiospectral image processing workflow considerations for advanced MR spectroscopy of the brain

Magnetic resonance spectroscopy (MRS) is one of the few non-invasive imaging modalities capable of making neurochemical and metabolic measurements in vivo. Traditionally, the clinical utility of MRS has been narrow. The most common use has been the "single-voxel spectroscopy" variant to discern the presence of a lactate peak in the spectra in one location in the brain, typically to evaluate for ischemia in neonates. Thus, the reduction of rich spectral data to a binary variable has not classically necessitated much signal processing. However, scanners have become more powerful and MRS sequences more advanced, increasing data complexity and adding 2 to 3 spatial dimensions in addition to the spectral one. The result is a spatially- and spectrally-variant MRS image ripe for image processing innovation. Despite this potential, the logistics for robustly accessing and manipulating MRS data across different scanners, data formats, and software standards remain unclear. Thus, as research into MRS advances, there is a clear need to better characterize its image processing considerations to facilitate innovation from scientists and engineers. Building on established neuroimaging standards, we describe a framework for manipulating these images that generalizes to the voxel, spectral, and metabolite level across space and multiple imaging sites while integrating with LCModel, a widely used quantitative MRS peak-fitting platform. In doing so, we provide examples to demonstrate the advantages of such a workflow in relation to recent publications and with new data. Overall, we hope our characterizations will lower the barrier of entry to MRS processing for neuroimaging researchers.

Quantification of GABA concentration measured noninvasively in the human posterior cingulate cortex with 7 T ultra-short-TE MR spectroscopy

PURPOSE

The increased spectral dispersion achieved at ultra-high field permits quantification of γ-aminobutyric acid (GABA) concentrations at ultra-short-TE without editing. This work investigated the influence of spectral quality and different LCModel fitting approaches on quantification of GABA. Additionally, the sensitivity with which cross-sectional and longitudinal variations in GABA concentrations can be observed was characterized.

METHODS

In - vivo spectra were acquired in the posterior cingulate cortex of 10 volunteers at 7 T using a STEAM sequence. Synthetically altered spectra with different levels of GABA signals were used to investigate the reliability of GABA quantification with different LCModel fitting approaches and different realizations of SNR. The synthetically altered spectra were also used to characterize the sensitivity of GABA quantification.

RESULTS

The best LCModel fitting approach used stiff spline baseline, no soft constraints, and measured macromolecules in the basis set. With lower SNR, coefficients of variation increased dramatically. Longitudinal and cross-sectional variations in GABA of 10% could be detected with 79 and 48 participants per group, respectively. However, the small cohort may bias the calculation of the coefficients of variation and of the sample size that would be needed to detect variations in GABA.

CONCLUSION

Reliable quantification of normal and abnormal GABA concentrations was achieved for high quality 7 T spectra using LCModel fitting.

In vivo magnetic resonance <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msup><mml:mrow/> <mml:mrow><mml:mn>31</mml:mn></mml:mrow> </mml:msup> </mml:mrow> </mml:math> P-Spectral Analysis With Neural Networks: 31P-SPAWNN

PURPOSE

We have introduced an artificial intelligence framework, 31P-SPAWNN, in order to fully analyze phosphorus-31 ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msup><mml:mrow/> <mml:mrow><mml:mn>31</mml:mn></mml:mrow> </mml:msup> </mml:mrow> <mml:annotation>$$ {}^{31} $$</mml:annotation></mml:semantics> </mml:math> P) magnetic resonance spectra. The flexibility and speed of the technique rival traditional least-square fitting methods, with the performance of the two approaches, are compared in this work.

THEORY AND METHODS

Convolutional neural network architectures have been proposed for the analysis and quantification of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msup><mml:mrow/> <mml:mrow><mml:mn>31</mml:mn></mml:mrow> </mml:msup> </mml:mrow> <mml:annotation>$$ {}^{31} $$</mml:annotation></mml:semantics> </mml:math> P-spectroscopy. The generation of training and test data using a fully parameterized model is presented herein. In vivo unlocalized free induction decay and three-dimensional <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msup><mml:mrow/> <mml:mrow><mml:mn>31</mml:mn></mml:mrow> </mml:msup> </mml:mrow> <mml:annotation>$$ {}^{31} $$</mml:annotation></mml:semantics> </mml:math> P-magnetic resonance spectroscopy imaging data were acquired from healthy volunteers before being quantified using either 31P-SPAWNN or traditional least-square fitting techniques.

RESULTS

The presented experiment has demonstrated both the reliability and accuracy of 31P-SPAWNN for estimating metabolite concentrations and spectral parameters. Simulated test data showed improved quantification using 31P-SPAWNN compared with LCModel. In vivo data analysis revealed higher accuracy at low signal-to-noise ratio using 31P-SPAWNN, yet with equivalent precision. Processing time using 31P-SPAWNN can be further shortened up to two orders of magnitude.

CONCLUSION

The accuracy, reliability, and computational speed of the method open new perspectives for integrating these applications in a clinical setting.

In Vitro 31P MR Chemical Shifts of In Vivo-Detectable Metabolites at 3T as a Basis Set for a Pilot Evaluation of Skeletal Muscle and Liver 31P Spectra with LCModel Software

Most in vivo ³¹P MR studies are realized on 3T MR systems that provide sufficient signal intensity for prominent phosphorus metabolites. The identification of these metabolites in the in vivo spectra is performed by comparing their chemical shifts with the chemical shifts measured in vitro on high-field NMR spectrometers. To approach in vivo conditions at 3T, a set of phantoms with defined metabolite solutions were measured in a 3T whole-body MR system at 7.0 and 7.5 pH, at 37 °C. A free induction decay (FID) sequence with and without ¹H decoupling was used. Chemical shifts were obtained of phosphoenolpyruvate (PEP), phosphatidylcholine (PtdC), phosphocholine (PC), phosphoethanolamine (PE), glycerophosphocholine (GPC), glycerophosphoetanolamine (GPE), uridine diphosphoglucose (UDPG), glucose-6-phosphate (G6P), glucose-1-phosphate (G1P), 2,3-diphosphoglycerate (2,3-DPG), nicotinamide adenine dinucleotide (NADH and NAD⁺), phosphocreatine (PCr), adenosine triphosphate (ATP), adenosine diphosphate (ADP), and inorganic phosphate (Pi). The measured chemical shifts were used to construct a basis set of ³¹P MR spectra for the evaluation of ³¹P in vivo spectra of muscle and the liver using LCModel software (linear combination model). Prior knowledge was successfully employed in the analysis of previously acquired in vivo data.

MR Spectroscopy of the Insula: Within- and between-Session Reproducibility of MEGA-PRESS Measurements of GABA+ and Other Metabolites

The insula plays a critical role in many neuropsychological disorders. Research investigating its neurochemistry with magnetic resonance spectroscopy (MRS) has been limited compared with cortical regions. Here, we investigate the within-session and between-session reproducibility of metabolite measurements in the insula on a 3T scanner. We measure N-acetylaspartate + N-acetylaspartylglutamate (tNAA), creatine + phosphocreatine (tCr), glycerophosphocholine + phosphocholine (tCho), myo-inositol (Ins), glutamate + glutamine (Glx), and γ-aminobutyric acid (GABA) in one cohort using a j-edited MEGA-PRESS sequence. We measure tNAA, tCr, tCho, Ins, and Glx in another cohort with a standard short-TE PRESS sequence as a reference for the reproducibility metrics. All participants were scanned 4 times identically: 2 back-to-back scans each day, on 2 days. Preprocessing was done using LCModel and Gannet. Reproducibility was determined using Pearson’s r, intraclass-correlation coefficients (ICC), coefficients of variation (CV%), and Bland–Altman plots. A MEGA-PRESS protocol requiring averaged results over two 6:45-min scans yielded reproducible GABA measurements (CV% = 7.15%). This averaging also yielded reproducibility metrics comparable to those from PRESS for the other metabolites. Voxel placement inconsistencies did not affect reproducibility, and no sex differences were found. The data suggest that MEGA-PRESS can reliably measure standard metabolites and GABA in the insula.

Acquisition and quantification pipeline for in vivo hyperpolarized 13 C MR spectroscopy

PURPOSE

The goal of this study was to combine a specialized acquisition method with a new quantification pipeline to accurately and efficiently probe the metabolism of hyperpolarized ¹³ C-labeled compounds in vivo. In this study, we tested our approach on [2-¹³ C]pyruvate and [1-¹³ C]α-ketoglutarate data in rat orthotopic brain tumor models at 3T.

METHODS

We used a multiband metabolite-specific radiofrequency (RF) excitation in combination with a variable flip angle scheme to minimize substrate polarization loss and measure fast metabolic processes. We then applied spectral-temporal denoising using singular value decomposition to enhance spectral quality. This was combined with LCModel-based automatic ¹³ C spectral fitting and flip angle correction to separate overlapping signals and rapidly quantify the different metabolites.

RESULTS

Denoising improved the metabolite signal-to-noise ratio (SNR) by approximately 5. It also improved the accuracy of metabolite quantification as evidenced by a significant reduction of the Cramer Rao lower bounds. Furthermore, the use of the automated and user-independent LCModel-based quantification approach could be performed rapidly, with the kinetic quantification of eight metabolite peaks in a 12-spectrum array achieved in less than 1 minute.

CONCLUSION

The specialized acquisition method combined with denoising and a new quantification pipeline using LCModel for the first time for hyperpolarized ¹³ C data enhanced our ability to monitor the metabolism of [2-¹³ C]pyruvate and [1-¹³ C]α-ketoglutarate in rat orthotopic brain tumor models in vivo. This approach could be broadly applicable to other hyperpolarized agents both preclinically and in the clinical setting.

Electrical status epilepticus in sleep: The role of thalamus in etiopathogenesis

PURPOSE

In patients diagnosed with epilepsy, decreased ratio of N-acetyl aspartate to creatine (NAA/Cr) measured in magnetic resonance spectroscopy (MRS) has been accepted as a sign of neuronal cell loss or dysfunction. In this study, we aimed to determine whether a similar neuronal cell loss is present in a group of encephalopathy with electrical status epilepticus in sleep (ESES) patients METHODS: We performed this case-control study at a tertiary pediatric neurology center with patients with ESES. Inclusion criteria for the patient group were as follows: 1) a spike-wave index of at least 50%, 2) acquired neuropsychological regression, 3) normal cranial MRI. Eventually, a total of 21 patients with ESES and 17 control subjects were enrolled in the study. MRI of all control subjects was also within normal limits. 3D Slicer program was used for the analysis of thalamic and brain volumes. LCModel spectral fitting software was used to analyze single-voxel MRS data from the right and left thalamus of the subjects.

RESULTS

The mean age was 8.0 ± 1.88 years and 8.3 ± 1.70 years in ESES patients and the control subjects. After correcting for the main potential confounders (age and gender) with a linear regression model, NAA/Creatine ratio of the right thalamus was significantly lower in the ESES patient group compared to the healthy control group (p = 0.026). Likewise, the left thalamus NAA/Cr ratio was significantly lower in the ESES patient group than the healthy control group (p = 0.007). After correcting for age and gender, right thalamic volume was not statistically significantly smaller in ESES patients than in healthy controls (p = 0.337), but left thalamic volume was smaller in ESES patients than in healthy controls (p = 0.024).

CONCLUSION

In ESES patients, the NAA/Creatine ratio, which is an indicator of neuronal cell loss or dysfunction in the right and left thalamus, which appears regular on MRI, was found to be significantly lower than the healthy control group. This metabolic-induced thalamic dysfunction, which was reported for the first time up to date, may play a role in ESES epileptogenesis.

Influence of fitting approaches in LCModel on MRS quantification focusing on age-specific macromolecules and the spline baseline

Quantification of neurochemical concentrations from ¹ H MR spectra is challenged by incomplete knowledge of contributing signals. Some experimental conditions hinder the acquisition of artifact-free spectra and impede the acquisition of condition-specific macromolecule (MM) spectra. This work studies differences caused by fitting solutions routinely employed to manage resonances from MM and lipids. High quality spectra (free of residual water and lipid artifacts and for which condition-specific MM spectra are available) are used to understand the influences of spline baseline flexibility and noncondition-specific MM on neurochemical quantification. Fitting with moderate spline flexibility or using noncondition-specific MM led to quantification that differed from when an appropriate, fully specified model was used. This occurred for all neurochemicals to an extent that varied in magnitude among and within approaches. The spline baseline was more tortuous when less constrained and when used in combination with noncondition-specific MM. Increasing baseline flexibility did not reproduce concentrations quantified under appropriate conditions when spectra were fitted using a MM spectrum measured from a mismatched cohort. Using the noncondition-specific MM spectrum led to quantification differences that were comparable in size with using a fitting model that had moderate freedom, and these influences were additive. Although goodness of fit was better with greater fitting flexibility, quantification differed from when fitting with a fully specified model that is appropriate for low noise data. Notable GABA and PE concentration differences occurred with lower estimates of measurement error when fitting with greater spline flexibility or noncondition-specific MM. These data support the need for improved metrics of goodness of fit. Attempting to correct for artifacts or absence of a condition-specific MM spectrum via increased spline flexibility and usage of noncondition-specific MM spectra cannot replace artifact-free data quantified with a condition-specific MM spectrum.

Repeatability and reliability of GABA measurements with magnetic resonance spectroscopy in healthy young adults

PURPOSE

Gamma-aminobutyric acid (GABA) abnormalities have been implicated in a range of neuropsychiatric disorders. Despite substantial interest in probing GABA in vivo, human imaging studies relying on magnetic resonance spectroscopy (MRS) have generally been hindered by technical challenges, including GABA's relatively low concentration and spectral overlap with other metabolites. Although past studies have shown moderate-to-strong test-retest repeatability and reliability of GABA within certain brain regions, many of these studies have been limited by small sample sizes.

METHODS

GABA+ (macromolecular-contaminated) test-retest reliability and repeatability were assessed via a Meshcher-Garwood point resolved spectroscopy (MEGA-PRESS) MRS sequence in the rostral anterior cingulate cortex (rACC; n = 21) and dorsolateral prefrontal cortex (dlPFC; n = 20) in healthy young adults. Data were collected on a 3T scanner (Siemens Prisma, Siemens Healthcare, Erlangen, Germany) and GABA+ results were reported in reference to both total creatine (GABA+/tCr) and water (GABA+/water).

RESULTS

Results showed strong test-retest repeatability (mean GABA+/tCr coefficient of variation [CV] = 4.6%; mean GABA+/water CV = 4.0%) and reliability (GABA+/tCr intraclass correlation coefficient [ICC] = 0.77; GABA+/water ICC = 0.87) in the dlPFC. The rACC showed acceptable (but comparatively lower) repeatability (mean GABA+/tCr CV = 8.0%; mean GABA+/water CV = 7.5%), yet low-moderate reliability (GABA+/tCr ICC = 0.40; GABA+/water ICC = 0.44).

CONCLUSION

The present study found excellent GABA+ MRS repeatability and reliability in the dlPFC. The rACC showed inferior results, possibly because of a combination of shimming impedance and measurement error. These data suggest that MEGA-PRESS can be utilized to reliably distinguish participants based on dlPFC GABA+ levels, whereas the mixed results in the rACC merit further investigation.

PRESS timings for resolving 13 C4 -glutamate 1 H signal at 9.4 T: Demonstration in rat with uniformly labelled 13 C-glucose

MRS of ¹³ C₄ -labelled glutamate (¹³ C₄ -Glu) during an infusion of a carbon-13 (¹³ C)-labelled substrate, such as uniformly labelled glucose ([U-¹³ C₆ ]-Glc), provides a measure of Glc metabolism. The presented work provides a single-shot indirect ¹³ C detection technique to quantify the approximately 2.51 ppm ¹³ C₄ -Glu satellite proton (¹ H) peak at 9.4 T. The methodology is an optimized point-resolved spectroscopy (PRESS) sequence that minimizes signal contamination from the strongly coupled protons of N-acetylaspartate (NAA), which resonate at approximately 2.49 ppm. J-coupling evolution of protons was characterized numerically and verified experimentally. A (TE₁ , TE₂ ) combination of (20 ms, 106 ms) was found to be suitable for minimizing NAA signal in the 2.51 ppm ¹ H ¹³ C₄ -Glu spectral region, while retaining the ¹³ C₄ -Glu ¹ H satellite peak. The efficacy of the technique was verified on phantom solutions and on two rat brains in vivo during an infusion of [U-¹³ C₆ ]-Glc. LCModel was employed for analysis of the in vivo spectra to quantify the 2.51 ppm ¹ H ¹³ C₄ -Glu signal to obtain Glu C4 fractional enrichment time courses during the infusions. Cramér-Rao lower bounds of about 8% were obtained for the 2.51 ppm ¹³ C₄ -Glu ¹ H satellite peak with the optimal TE combination.

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

Glutamine (Gln), glutamate (Glu) and γ-aminobutyric acid (GABA) are relevant brain metabolites that can be measured with magnetic resonance spectroscopy (MRS). This work optimizes the point-resolved spectroscopy (PRESS) sequence echo times, TE₁ and TE₂ , for improved simultaneous quantification of the three metabolites at 9.4 T. Quantification was based on the proton resonances of Gln, Glu and GABA at ≈2.45, ≈2.35 and ≈2.28 ppm, respectively. Glu exhibits overlap with both Gln and GABA; in addition, the Gln peak is contaminated by signal from the strongly coupled protons of N-acetylaspartate (NAA), which resonate at about 2.49 ppm. J-coupling evolution of the protons was characterized numerically and verified experimentally. A {TE₁ , TE₂ } combination of {106 ms, 16 ms} minimized the NAA signal in the Gln spectral region, whilst retaining Gln, Glu and GABA peaks. The efficacy of the technique was verified on phantom solutions and on rat brain in vivo. LCModel was employed to analyze the in vivo spectra. The average T₂ -corrected Gln, Glu and GABA concentrations were found to be 3.39, 11.43 and 2.20 mM, respectively, assuming a total creatine concentration of 8.5 mM. LCModel Cramér-Rao lower bounds (CRLBs) for Gln, Glu and GABA were in the ranges 14-17%, 4-6% and 16-19%, respectively. The optimal TE resulted in concentrations for Gln and GABA that agreed more closely with literature concentrations compared with concentrations obtained from short-TE spectra acquired with a {TE₁ , TE₂ } combination of {12 ms, 9 ms}. LCModel estimations were also evaluated with short-TE PRESS and with the optimized long TE of {106 ms, 16 ms}, using phantom solutions of known metabolite concentrations. It was shown that concentrations estimated with LCModel can be inaccurate when combined with short-TE PRESS, where there is peak overlap, even when low (<20%) CRLBs are reported.

Sensitivity and specificity of human brain glutathione concentrations measured using short-TE (1)H MRS at 7 T

Although the MR editing techniques that have traditionally been used for the measurement of glutathione (GSH) concentrations in vivo address the problem of spectral overlap, they suffer detriments associated with inherently long TEs. The purpose of this study was to characterize the sensitivity and specificity for the quantification of GSH concentrations without editing at short TE. The approach was to measure synthetically generated changes in GSH concentrations from in vivo stimulated echo acquisition mode (STEAM) spectra after in vitro GSH spectra had been added to or subtracted from them. Spectra from five test subjects were synthetically altered to mimic changes in the GSH signal. To account for different background noise between measurements, retest spectra (from the same individuals as used to generate the altered data) and spectra from five other individuals were compared with the synthetically altered spectra to investigate the reliability of the quantification of GSH concentration. Using STEAM spectroscopy at 7 T, GSH concentration differences on the order of 20% were detected between test and retest studies, as well as between differing populations in a small sample (n = 5) with high accuracy (R(2) > 0.99) and certainty (p ≤ 0.01). Both increases and decreases in GSH concentration were reliably quantified with small impact on the quantification of ascorbate and γ-aminobutyric acid. These results show the feasibility of using short-TE (1)H MRS to measure biologically relevant changes and differences in human brain GSH concentration. Although these outcomes are specific to the experimental approach used and the spectral quality achieved, this study serves as a template for the analogous scrutiny of quantification reliability for other compounds, methodologies and spectral qualities.

Quantification of in vivo ³¹P NMR brain spectra using LCModel

Quantification of (31)P NMR spectra is commonly performed using line-fitting techniques with prior knowledge. Currently available time- and frequency-domain analysis software includes AMARES (in jMRUI) and CFIT respectively. Another popular frequency-domain approach is LCModel, which has been successfully used to fit both (1)H and (13)C in vivo NMR spectra. To the best of our knowledge LCModel has not been used to fit (31)P spectra. This study demonstrates the feasibility of using LCModel to quantify in vivo (31)P MR spectra, provided that adequate prior knowledge and LCModel control parameters are used. Both single-voxel and MRSI data are presented, and similar results are obtained with LCModel and with AMARES. This provides a new method for automated, operator-independent analysis of (31)P NMR spectra.

Influence of echo time in quantitative proton MR spectroscopy using LCModel

OBJECTIVE

The objective of this study was to elucidate the influence on quantitative analysis using LCModel with the condition of echo time (TE) longer than the recommended values in the spectrum acquisition specifications.

METHODS

A 3T magnetic resonance system was used to perform proton magnetic resonance spectroscopy. The participants were 5 healthy volunteers and 11 patients with glioma. Data were collected at TE of 72, 144 and 288ms. LCModel was used to quantify several metabolites (N-acetylaspartate, creatine and phosphocreatine, and choline-containing compounds). The results were compared with quantitative values obtained by using the T2-corrected internal reference method.

RESULTS

In healthy volunteers, when TE was long, the quantitative values obtained using LCModel were up to 6.8-fold larger (p<0.05) than those obtained using the T2-corrected internal reference method. The ratios of the quantitative values obtained by the two methods differed between metabolites (p<0.05). In patients with glioma, the ratios of quantitative values obtained by the two methods tended to be larger at longer TE, similarly to the case of healthy volunteers, and large between-individual variation in the ratios was observed.

CONCLUSIONS

In clinical practice, TE is sometimes set longer than the value recommended for LCModel. If TE is long, LCModel overestimates the quantitative value since it cannot compensate for signal attenuation, and this effect is different for each metabolite and condition. Therefore, if TE is longer than recommended, it is necessary to account for the possibly reduced reliability of quantitative values calculated using LCModel.

(1) H 2D MRSI tissue type analysis of gliomas

PURPOSE

To decompose 1H MR spectra of glioma patients into normal and abnormal tissue proportions for tumor classification and delineation.

METHODS

Anatomical imaging and 1H magnetic resonance spectroscopic imaging data have been acquired from 11 grade II and 13 grade IV glioma patients. LCModel was used to decompose the magnetic resonance spectroscopic imaging data into normal brain, grade II, and grade IV tissue proportions using a tissue type basis set. Simulations were conducted to evaluate the accuracy of the methodology. Results were visualized using colormaps and abnormality contours showing tumor grade and extent.

RESULTS

Simulations suggest that infiltrative tumor proportions as low as 20% can be identified at the typical 1H magnetic resonance spectroscopy signal-to-noise found in vivo. Tumor grading according to the highest estimated tumor grade within a lesion gave a classification accuracy of 86% discriminating between grade II and grade IV glioma. Voxels with significant proportions of tumor type spectra were found beyond the margins of contrast enhancement for most grade IV cases consistent with infiltration whereas the abnormality contours show that some tumors are confined within the hyperintensities shown by both post contrast T1 weighted and T2 weighted imaging.

CONCLUSION

LCModel can be used to decompose 1H MR spectra into proportions of normal and abnormal tissue to identify tumor extent, infiltration, and overall grade.

Glutamate quantification in patients with supratentorial gliomas using chemical shift imaging

This study aimed to evaluate and validate chemical shift imaging (CSI) for in vivo glutamate (Glu) quantification in patients with supratentorial gliomas. If validated, CSI could become an extremely useful tool to investigate metabolic dysfunction of Glu in excitotoxic neuropathologies. Quantitative CSI estimates of Glu concentrations were compared with known concentrations of Glu in aqueous phantom solutions. Forty-one patients with known or likely supratentorial gliomas underwent preoperative CSI. The spectra obtained were analyzed for Glu concentrations and Glu to creatine (Cr) ratios. These in vivo measurements were correlated against ex vivo Glu content quantified by high performance liquid chromatography (HPLC) measured in 65 resected brain tumor and peritumoral brain specimens. For the phantom solutions the CSI estimates of Glu concentration and the Glu/Cr ratios were highly correlated with known Glu concentration (r²  = 0.95, p = 0.002, and r²  = 0.97, p < 0.0001, respectively). There was a modest, but statistically significant, correlation between the ex vivo measured Glu and in vivo spectroscopic Glu concentration (r²  = 0.22, p = 0.04) and ratios of Glu to Cr (r²  = 0.30, p = 0.002). Quantitative measurement of Glu content is feasible in patients with supratentorial gliomas using CSI. The in vitro and in vivo results suggest that this has the potential to be a reliable quantitative imaging assay for brain tumor patients. This may have wide clinical research applications in a number of neurological disorders where Glu excitotoxicity and metabolic dysfunction are known to play a role in pathogenesis, including tumor associated epilepsy, epilepsy, stroke and neurotrauma.

Different quantification algorithms may lead to different results: a comparison using proton MRS lipid signals

Proton magnetic resonance spectroscopy (MRS) is a sensitive method for investigating the biochemical compounds in a tissue. The interpretation of the data relies on the quantification algorithms applied to MR spectra. Each of these algorithms has certain underlying assumptions and may allow one to incorporate prior knowledge, which could influence the quality of the fit. The most commonly considered types of prior knowledge include the line-shape model (Lorentzian, Gaussian, Voigt), knowledge of the resonating frequencies, modeling of the baseline, constraints on the damping factors and phase, etc. In this article, we study whether the statistical outcome of a biological investigation can be influenced by the quantification method used. We chose to study lipid signals because of their emerging role in the investigation of metabolic disorders. Lipid spectra, in particular, are characterized by peaks that are in most cases not Lorentzian, because measurements are often performed in difficult body locations, e.g. in visceral fats close to peristaltic movements in humans or very small areas close to different tissues in animals. This leads to spectra with several peak distortions. Linear combination of Model spectra (LCModel), Advanced Method for Accurate Robust and Efficient Spectral fitting (AMARES), quantitation based on QUantum ESTimation (QUEST), Automated Quantification of Short Echo-time MRS (AQSES)-Lineshape and Integration were applied to simulated spectra, and area under the curve (AUC) values, which are proportional to the quantity of the resonating molecules in the tissue, were compared with true values. A comparison between techniques was also carried out on lipid signals from obese and lean Zucker rats, for which the polyunsaturation value expressed in white adipose tissue should be statistically different, as confirmed by high-resolution NMR measurements (considered the gold standard) on the same animals. LCModel, AQSES-Lineshape, QUEST and Integration gave the best results in at least one of the considered groups of simulated or in vivo lipid signals. These outcomes highlight the fact that quantification methods can influence the final result and its statistical significance.

Effect of Carr-Purcell refocusing pulse trains on transverse relaxation times of metabolites in rat brain at 9.4 Tesla

PURPOSE

To investigate the effect of Carr-Purcell (CP) pulse trains on transverse relaxation times, T2, of tissue water and metabolites (both noncoupled and J-coupled spins) in the rat brain at 9.4 Tesla (T) using LASER, CP-LASER, and T2ρ-LASER sequences.

METHODS

Proton NMR spectra were measured in rat brain in vivo at 9.4T. Spectra were acquired at multiple echo times ranging from 18 to 402 ms. All spectra were analyzed using LCModel with simulated basis sets. Signals of metabolites as a function of echo time were fitted using a mono-exponential function to determine their T2 relaxation times.

RESULTS

Measured T2 s for tissue water and all metabolites were significantly longer with CP-LASER and T2ρ-LASER compared with LASER. The T2 increased by a factor of ∼ 1.3 for noncoupled and weakly coupled spins (e.g., N-acetylaspartate and total creatine) and by a factor of ∼ 2 (e.g., glutamine and taurine) to ∼ 4 (e.g., glutamate and myo-inositol) for strongly coupled spins.

CONCLUSION

Application of a CP pulse train results in a larger increase in T2 relaxation times for strongly coupled spins than for noncoupled (singlet) and weakly coupled spins. This needs to be taken into account when correcting for T2 relaxation in CP-like sequences such as LASER.

jSIPRO - analysis tool for magnetic resonance spectroscopic imaging

Magnetic resonance spectroscopic imaging (MRSI) involves a huge number of spectra to be processed and analyzed. Several tools enabling MRSI data processing have been developed and widely used. However, the processing programs primarily focus on sophisticated spectra processing and offer limited support for the analysis of the calculated spectroscopic maps. In this paper the jSIPRO (java Spectroscopic Imaging PROcessing) program is presented, which is a java-based graphical interface enabling post-processing, viewing, analysis and result reporting of MRSI data. Interactive graphical processing as well as protocol controlled batch processing are available in jSIPRO. jSIPRO does not contain a built-in fitting program. Instead, it makes use of fitting programs from third parties and manages the data flows. Currently, automatic spectra processing using LCModel, TARQUIN and jMRUI programs are supported. Concentration and error values, fitted spectra, metabolite images and various parametric maps can be viewed for each calculated dataset. Metabolite images can be exported in the DICOM format either for archiving purposes or for the use in neurosurgery navigation systems.

Group independent component analysis of MR spectra

This study investigates the potential of independent component analysis (ICA) to provide a data-driven approach for group level analysis of magnetic resonance (MR) spectra. ICA collectively analyzes data to identify maximally independent components, each of which captures covarying resonances, including those from different metabolic sources. A comparative evaluation of the ICA approach with the more established LCModel method in analyzing two different noise-free, artifact-free, simulated data sets of known compositions is presented. The results from such ideal simulations demonstrate the ability of data-driven ICA to decompose data and accurately extract components resembling modeled basis spectra from both data sets, whereas the LCModel results suffer when the underlying model deviates from assumptions, thus highlighting the sensitivity of model-based approaches to modeling inaccuracies. Analyses with simulated data show that independent component weights are good estimates of concentrations, even of metabolites with low intensity singlet peaks, such as scyllo-inositol. ICA is also applied to single voxel spectra from 193 subjects, without correcting for baseline variations, line-width broadening or noise. The results provide evidence that, despite the presence of confounding artifacts, ICA can be used to analyze in vivo spectra and extract resonances of interest. ICA is a promising technique for decomposing MR spectral data into components resembling metabolite resonances, and therefore has the potential to provide a data-driven alternative to the use of metabolite concentrations derived from curve-fitting individual spectra in making group comparisons.