MRS signal quantitation: a review of time- and frequency-domain methods

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.

Low-field permanent magnets for industrial process and quality control

In this review we focus on the technology associated with low-field NMR. We present the current state-of-the-art in low-field NMR hardware and experiments, considering general magnet designs, rf performance, data processing and interpretation. We provide guidance on obtaining the optimum results from these instruments, along with an introduction for those new to low-field NMR. The applications of lowfield NMR are now many and diverse. Furthermore, niche applications have spawned unique magnet designs to accommodate the extremes of operating environment or sample geometry. Trying to capture all the applications, methods, and hardware encompassed by low-field NMR would be a daunting task and likely of little interest to researchers or industrialists working in specific subject areas. Instead we discuss only a few applications to highlight uses of the hardware and experiments in an industrial environment. For details on more particular methods and applications, we provide citations to specialized review articles.

Mapping of prostate cancer by 1H MRSI

In many studies, it has been demonstrated that (1)H MRSI of the human prostate has great potential to aid prostate cancer management, e.g. in the detection and localisation of cancer foci in the prostate or in the assessment of its aggressiveness. It is particularly powerful in combination with T2 -weighted MRI. Nevertheless, the technique is currently mainly used in a research setting. This review provides an overview of the state-of-the-art of three-dimensional MRSI, including the specific hardware required, dedicated data acquisition sequences and information on the spectral content with background on the MR-visible metabolites. In clinical practice, it is important that relevant MRSI results become available rapidly, reliably and in an easy digestible way. However, this functionality is currently not fully available for prostate MRSI, which is a major obstacle for routine use by inexperienced clinicians. Routine use requires more automation in the processing of raw data than is currently available. Therefore, we pay specific attention in this review on the status and prospects of the automated handling of prostate MRSI data, including quality control. The clinical potential of three-dimensional MRSI of the prostate is illustrated with literature examples on prostate cancer detection, its localisation in the prostate, its role in the assessment of cancer aggressiveness and in the selection and monitoring of therapy.

Extrapolation and phase correction of non-uniformly broadened signals

The initial part of FID-signals cannot always be acquired experimentally. This is particularly true for signals characterised by strong inhomogeneous broadening, such as those in porous materials, e.g. cements, soils and rocks, those measured by portable NMR-apparatus, or EPR-signals. Here we report on a numerical method we designed to extrapolate those initial missing parts, i.e. to retrieve their amplitude and phase. Should the entire signal be available from an experiment, the algorithm can still be used as an automatic phase-corrector and a low-pass filter. The method is based on the use of cardinal series, applies to any oversampled signals and requires no prior knowledge of the system under study. We show that the method can also be used to restore entire one-dimensional MRI-data sets from those in which less than half of the k-space was sampled, thus not only potentially allowing to speed up data acquisition - when extended to two or three dimensions, but also to circumvent phase-distortions usually encountered when exploring the k-space near its origin.

Metabolomic approach to human brain spectroscopy identifies associations between clinical features and the frontal lobe metabolome in multiple sclerosis

Proton magnetic resonance spectroscopy ((1)H-MRS) is capable of noninvasively detecting metabolic changes that occur in the brain tissue in vivo. Its clinical utility has been limited so far, however, by analytic methods that focus on independently evaluated metabolites and require prior knowledge about which metabolites to examine. Here, we applied advanced computational methodologies from the field of metabolomics, specifically partial least squares discriminant analysis and orthogonal partial least squares, to in vivo (1)H-MRS from frontal lobe white matter of 27 patients with relapsing-remitting multiple sclerosis (RRMS) and 14 healthy controls. We chose RRMS, a chronic demyelinating disorder of the central nervous system, because its complex pathology and variable disease course make the need for reliable biomarkers of disease progression more pressing. We show that in vivo MRS data, when analyzed by multivariate statistical methods, can provide reliable, distinct profiles of MRS-detectable metabolites in different patient populations. Specifically, we find that brain tissue in RRMS patients deviates significantly in its metabolic profile from that of healthy controls, even though it appears normal by standard MRI techniques. We also identify, using statistical means, the metabolic signatures of certain clinical features common in RRMS, such as disability score, cognitive impairments, and response to stress. This approach to human in vivo MRS data should promote understanding of the specific metabolic changes accompanying disease pathogenesis, and could provide biomarkers of disease progression that would be useful in clinical trials.

Increasing sensitivity in determining chemical shifts in one dimensional Lorentzian NMR spectra

An algorithm is presented for one-dimensional NMR systems that employs nonlinear, non-Fourier methods to convert noisy time-dependent free induction decay (FID) data to a denoised frequency spectrum that gives reliable chemical shifts and coupling constants when the spectrum is Lorentzian. It is formulated in a way that increases frequency sensitivity and resolution and, for nuclei of low natural abundance, potentially avoids enrichment totally or in part. The algorithm should also be of use in analytical chemistry where enrichment is not possible. In effect, the useful limit of detection is significantly lowered. The algorithm uses new "phasing" and "feature stability upon accumulation" methods to reliably separate signal from noise at low signal-to-noise ratios where the Fourier spectrum requires many more transients to be definitive as to what is signal and what is noise. The long-standing problem of "false features" that plagued many prior attempts to employ nonlinear methods is thereby resolved for Lorentzian spectra. Examples are reported, and the limitations of the algorithm are discussed.

Quality control of prostate 1 H MRSI data

MRSI of prostate cancer provides a potential clinical tool to aid in the detection and characterisation of this disease, but its clinical use is limited by the need for the specialist training of radiologists to read these datasets. An essential part of this reading is the assessment of the usability and reliability of MRSI spectra because they can be affected by artefacts such as poor signal to noise, lipid signal contamination and broad resonances that could cause errors of interpretation. We have developed an automated quality control algorithm that classifies every voxel of an MRSI dataset as either acceptable or unacceptable for further analysis, based on the spectral profile alone. The method was trained and tested based on a gold standard of agreement of four experts. It was highly accurate: testing with a novel set of data from MRSI patients produced agreement with the experts' consensus decisions with a specificity of 0.95 and sensitivity of 0.95. This method provides fast quality control of three-dimensional MRSI datasets of the prostate, removing the need for radiologists to perform this time consuming, but necessary, task prior to further analysis.

Measuring glucose concentrations in the rat brain using echo-time-averaged point resolved spectroscopy at 7 tesla

Glucose has multiple functions in the brain, and there is interest in estimating in vivo concentrations rather than merely the uptake determined by nuclear medicine. Glucose can be estimated using magnetic resonance spectroscopy, but measurement is difficult due to its multiple J-coupled proton signals overlapping with other metabolite signals. To minimize the effect of interfering signals, echo time (TE) values between 60 and 95 ms were averaged, and the loss in signal due to the T2 effect was corrected in both the estimation of glucose concentration and in creation of the basis files for fitting. The effectiveness of the TE-averaging method was evaluated by measuring the glucose concentration in fasted rats before and after feeding. The brain glucose in all rats increased after feeding with fasted and fed glucose-to-creatine ratios of 0.15 ± 0.03 and 0.24 ± 0.04, respectively. Data at a short TE of 13 ms measured ratios of 0.30 ± 0.16 and 0.36 ± 0.24 for the fasted and fed rats, respectively, demonstrating the difficulty in obtaining reliable glucose measurements at short TE. Overall, TE averaging minimizes the influence of macromolecular signals and nearby peaks to give precise, consistent estimates of glucose.

MR proton spectroscopy for myocardial lipid deposition quantification: a quantitative comparison between 1.5T and 3T

PURPOSE

To evaluate 3T magnetic resonance spectroscopy (MRS)-derived myocardial fat-signal fractions in comparison with those from 1.5T MRS.

MATERIALS AND METHODS

We conducted phantom, ex vivo and in vivo myocardial specimen evaluations at both 1.5T and 3T using (1)H-MRS. A phantom with nine fat-water emulsions was constructed to assess the accuracy of the spectroscopy measurements. Ex vivo spectroscopy data were acquired in 70 segments from 21 autopsy heart slices. In vivo spectroscopy data were acquired in the interventricular septum from 22 human volunteers.

RESULTS

Phantom experiments demonstrated that 1.5T and 3T measurements were highly correlated with the reference values (r = 0.78, P < 0.05). The ex vivo and in vivo experiments demonstrated an increase in signal-to-noise ratio (SNR) of 45 ± 73% and 76 ± 72% at 3T compared to 1.5T (P < 0.05). The mean fat-signal fraction was similar at 3T and 1.5T (1.11 ± 1.18 vs. 1.00 ± 1.09, respectively, P = NS) in ex vivo studies but were significantly different in the in vivo studies (2.47 ± 1.46 vs. 1.56 ± 1.34, P < 0.05). The fat-signal fractions from 3T and 1.5T correlated fairly well in all experiments.

CONCLUSION

3T MRS has significantly greater SNR and could potentially be more accurate as compared to 1.5T for quantification of myocardial fat fraction in in vivo studies.

Metabolite Mapping with Extended Brain Coverage Using a Fast Multisection MRSI Pulse Sequence and a Multichannel Coil

Multisection magnetic resonance spectroscopic imaging is a widely used pulse sequence that has distinct advantages over other spectroscopic imaging sequences, such as dynamic shimming, large region-of-interest coverage within slices, and rapid data acquisition. It has limitations, however, in the number of slices that can be acquired in realistic scan times and information loss from spacing between slices. In this paper, we synergize the multi-section spectroscopic imaging pulse sequence with multichannel coil technology to overcome these limitations. These combined techniques now permit elimination of the gaps between slices and acquisition of a larger number of slices to realize the whole brain metabolite mapping without incurring the penalties of longer repetition times (and therefore longer acquisition times) or lower signal-to-noise ratios.

Decimative Spectral Estimation with Unconstrained Model Order

This paper presents a new state-space method for spectral estimation that performs decimation by any factor, it makes use of the full set of data and brings further apart the poles under consideration, while imposing almost no constraints to the size of the Hankel matrix (model order), as decimation increases. It is compared against two previously proposed techniques for spectral estimation (along with derived decimative versions), that lie among the most promising methods in the field of spectroscopy, where accuracy of parameter estimation is of utmost importance. Moreover, it is compared against a state-of-the-art purely decimative method proposed in literature. Experiments performed on simulated NMR signals prove the new method to be more robust, especially for low signal-to-noise ratio.

Cardinal series to restore NMR-signals dominated by strong inhomogeneous broadening

We have devised two numerical methods of restoring incomplete band-limited NMR-signals to integrity by either interpolating or extrapolating them. Both methods are based on use of the finite cardinal series, whose filtering properties were discussed previously, to model signals. They require no prior knowledge about the system under study, but only that the available parts of the signal were oversampled enough. The methods were tested on two types of computer-simulated signal. It proved superior to the linear prediction methods and Lagrange interpolation when applied to signals measured in highly inhomogeneous magnetic fields. The extrapolation method was then applied to restore experimentally-measured refocused FID-signals of a porous medium. The missing parts of the signal of up to several times the size of its Nyquist period could be recovered by either method.

Improved initial value estimation for short echo time magnetic resonance spectroscopy spectral analysis using short T2 signal attenuation

Robust spectral analysis of magnetic resonance spectroscopy data frequently uses a spectral model with prior metabolite signal information within a nonlinear least squares optimization algorithm. Starting values for the spectral model greatly influence the final results. Short echo time magnetic resonance spectroscopy contains broad signals that overlap with metabolite signals, complicating the estimation of starting values. We describe a method for more robust initial value estimation using a filter to attenuate short T(2) signal contributions (e.g., macromolecules or residual lipids). The method attenuates signals by truncating early points in the data set. Metabolite peak estimation is simplified by the removal of broad, short T(2) signals, and corrections for metabolite signal truncation are described. Short echo time simulated Monte Carlo data and in vivo data were used to validate the method. Areas for metabolite signals in the Monte Carlo data with singlet (N-acetylaspartate, creatine, choline) and singlet-like (myo-inositol) resonances were estimated within 10% of actual value for various metabolite line widths, signal-to-noise ratios, and underlying broad signal contributions. Initial value estimates of in vivo magnetic resonance spectroscopy data were within 14% of metabolite area ratios relative to the creatine peak fitted using established magnetic resonance spectroscopy spectral analysis software.

A constrained least-squares approach to the automated quantitation of in vivo ¹H magnetic resonance spectroscopy data

Totally Automatic Robust Quantitation in NMR (TARQUIN), a new method for the fully automatic analysis of short echo time in vivo (1)H Magnetic resonance spectroscopy is presented. Analysis is performed in the time domain using non-negative least squares, and a new method for applying soft constraints to signal amplitudes is used to improve fitting stability. Initial point truncation and Hankel singular value decomposition water removal are used to reduce baseline interference. Three methods were used to test performance. First, metabolite concentrations from six healthy volunteers at 3 T were compared with LCModel™. Second, a Monte-Carlo simulation was performed and results were compared with LCModel™ to test the accuracy of the new method. Finally, the new algorithm was applied to 1956 spectra, acquired clinically at 1.5 T, to test robustness to noisy, abnormal, artifactual, and poorly shimmed spectra. Discrepancies of less than approximately 20% were found between the main metabolite concentrations determined by TARQUIN and LCModel™ from healthy volunteer data. The Monte-Carlo simulation revealed that errors in metabolite concentration estimates were comparable with LCModel™. TARQUIN analyses were also found to be robust to clinical data of variable quality. In conclusion, TARQUIN has been shown to be an accurate and robust algorithm for the analysis of magnetic resonance spectroscopy data making it suitable for use in a clinical setting.

Towards automatic metabolomic profiling of high-resolution one-dimensional proton NMR spectra

Nuclear magnetic resonance (NMR) and Mass Spectroscopy (MS) are the two most common spectroscopic analytical techniques employed in metabolomics. The large spectral datasets generated by NMR and MS are often analyzed using data reduction techniques like Principal Component Analysis (PCA). Although rapid, these methods are susceptible to solvent and matrix effects, high rates of false positives, lack of reproducibility and limited data transferability from one platform to the next. Given these limitations, a growing trend in both NMR and MS-based metabolomics is towards targeted profiling or "quantitative" metabolomics, wherein compounds are identified and quantified via spectral fitting prior to any statistical analysis. Despite the obvious advantages of this method, targeted profiling is hindered by the time required to perform manual or computer-assisted spectral fitting. In an effort to increase data analysis throughput for NMR-based metabolomics, we have developed an automatic method for identifying and quantifying metabolites in one-dimensional (1D) proton NMR spectra. This new algorithm is capable of using carefully constructed reference spectra and optimizing thousands of variables to reconstruct experimental NMR spectra of biofluids using rules and concepts derived from physical chemistry and NMR theory. The automated profiling program has been tested against spectra of synthetic mixtures as well as biological spectra of urine, serum and cerebral spinal fluid (CSF). Our results indicate that the algorithm can correctly identify compounds with high fidelity in each biofluid sample (except for urine). Furthermore, the metabolite concentrations exhibit a very high correlation with both simulated and manually-detected values.

Semi-parametric time-domain quantification of HR-MAS data from prostate tissue

High Resolution--Magic Angle Spinning (HR-MAS) spectroscopy provides rich biochemical profiles that require accurate quantification to permit biomarker identification and to understand the underlying pathological mechanisms. Meanwhile, quantification of HR-MAS data from prostate tissue samples is challenging due to significant overlap between the resonant peaks, the presence of short T₂* metabolites such as citrate or polyamines (T₂ from 25 to 100 msec) and macromolecules, and variations in chemical shifts and T₂*s within a metabolite's spin systems. Since existing methods do not address these challenges completely, a new quantification method was developed and optimized for HR-MAS data acquired with an ultra short T(E) and over 30,000 data points. The proposed method, named HR-QUEST (High Resolution--QUEST), iteratively employs the QUEST time-domain semi-parametric strategy with a new model function that incorporates prior knowledge from whole and subdivided metabolite signals. With these features, HR-QUEST is able to independently fit the chemical shifts and T₂*s of a metabolite's spin systems, a necessity for HR-MAS data. By using the iterative fitting approach, it is able to account for significant contributions from macromolecules and to handle shorter T₂ metabolites, such as citrate and polyamines. After subdividing the necessary metabolite basis signals, the root mean square (RMS) of the residual was reduced by 52% for measured HR-MAS data from prostate tissue. Monte Carlo studies on simulated spectra with varied macromolecular contributions showed that the iterative fitting approach (6 iterations) coupled with inclusion of long T₂ macromolecule components in the basis set improve the quality of the fit, as assessed by the reduction of the RMS of the residual and of the RMS error of the metabolite signal estimate, by 27% and 71% respectively. With this optimized configuration, HR-QUEST was applied to measured HR-MAS prostate data and reliably quantified 16 metabolites and reference signals with estimated Cramér Rao Bounds ≤5%.

Investigation of adipose tissues in Zucker rats using in vivo and ex vivo magnetic resonance spectroscopy

In vivo single-voxel magnetic resonance spectroscopy (MRS) at 4.7T and ex vivo high-resolution proton magnetic resonance spectroscopy (HR-NMR) at 500 MHz were used to study the composition of adipose tissues in Zucker obese and Zucker lean rats. Lipid composition was characterized by unsaturation and polyunsaturation indexes and mean chain lengths. In vitro experiments were conducted in known mixtures of triglycerides and oils in order to validate the method. To avoid inaccuracies due to partial peak overlapping in MRS, peak quantification was performed after fitting of spectral peaks by using the QUEST algorithm. The intensity of different spectral lines was also corrected for T2 relaxation. Albeit with different sensitivity and accuracy, both techniques revealed that white adipose tissue is characterized by lower unsaturation and polyunsaturation indexes in obese rats compared with controls. HR-NMR revealed similar differences in brown adipose tissue. The present findings confirm the hypothesis that obese and lean Zucker rats have different adipose tissue composition.

Quantitative (1) H MR spectroscopic imaging of the prostate gland using LCModel and a dedicated basis-set: correlation with histologic findings

Proton magnetic resonance spectroscopic imaging ((1) H-MRSI) has been advocated as a valuable tool for prostate cancer diagnosis. However, a barrier to widespread clinical use of this technique is the lack of robust quantification methods that yield reproducible results in an institution-independent manner. The main goal of this study was to develop a standardized and fully automated approach (LCModel-based) for quantitative prostate (1) H-MRSI. To this end, a dedicated basis set was constructed by the combination of simulated (citrate, Cit; choline, Cho, and creatine, CR) and experimentally acquired (spermine, Spm) spectra. The overlapping Spm, Cho, and Cr could be resolved and quantified individually, thus allowing for the independent assessment of glandular (Cit and Spm) and proliferative (Cho) components. Several metabolite ratios were calculated and compared to the histologic findings of prostatectomy specimens from 10 prostate cancer patients with Gleason scores (3 + 3) and (3 + 4). The Cho mole fraction and the Cho/(Cit + Spm) ratio were found to best discriminate between prostate cancer and healthy tissue. The comparison between the quantitative MRSI results and the histologic findings suggests that no correlation exists between the detected metabolic alterations and the Gleason score of low-grade tumors.

Application of a Bayesian deconvolution approach for high-resolution (1)H NMR spectra to assessing the metabolic effects of acute phenobarbital exposure in liver tissue

High-resolution (1)H NMR spectroscopy is frequently used in the field of metabolomics to assess the metabolites found in biofluids or tissue extracts to define a metabolic profile that describes a given biological process. In this study, we aimed to increase the utility of NMR-based metabolomics by using advanced Bayesian modeling of the time-domain high-resolution 1D NMR free induction decay (FID). The improvement over traditional nonparametric binning is twofold and associated with enhanced resolution of the analysis and automation of the signal processing stage. The automation is achieved by using a Bayesian formalism for all parameters of the model including the number of components. The approach is illustrated with a study of early markers of acute exposure to different doses of a well-characterized nongenotoxic hepatocarcinogen, phenobarbital, in rats. The results demonstrate that Bayesian deconvolution produces a better model for the NMR spectra that allows the identification of subtle changes in metabolic concentrations and a decrease in the expected false discovery rate compared with approaches based on "binning". These properties suggest that Bayesian deconvolution could facilitate the biomarker discovery process and improve information extraction from high-resolution NMR spectra.

Improved determination of FID signal parameters in low-field NMR

In this work, novel methods are suggested for assessing signal parameters of the free induction decay (FID) in nuclear magnetic resonance (NMR) experiments. The FID signal was recorded in a microtesla field and analysed to determine its relaxation time, amplitude, Larmor frequency and phase. The challenge was posed by the narrow line width, whose related effects were investigated through simulations, also. The developed methods give a new view on FID signal estimation in microtesla as well as lower and higher fields. It is shown that the transverse relaxation time of a sample can be accurately determined in the frequency domain by other means than the Lorentz peak half width. Also, with some realistic approximations, a simple functional form for the power spectrum Lorentz peak shape is proposed. As shown in this work, the inspection of the power spectrum instead of the absorption and dispersion Lorentzians is advantageous in the sense that the waveform is independent of the FID phase. The automatic and efficient methods presented in this work incorporate an integral exponential fit, the fit of the power spectrum Lorentz peak and two ways to determine the FID phase. When there are sufficiently many data points in the Lorentz peak, the power spectrum Lorentz peak shape fit provides a quick, simple and accurate way of determining the amplitude, relaxation time and Larmor frequency of the FID. In the measurements of this work, however, the narrow line width led to establishing a more applicable method which is based on the exponential decay of the Lorentz peak with a temporally moving power spectrum window.

A sparse representation method for magnetic resonance spectroscopy quantification

In this paper, a sparse representation method is proposed for magnetic resonance spectroscopy (MRS) quantification. An observed MR spectrum is composed of a set of metabolic spectra of interest, a baseline and a noise. To separate the spectra of interest, the a priori knowledge about these spectra, such as signal models, the peak frequencies, and linewidth ranges of different resonances, is first integrated to construct a dictionary. The separation of the spectra of interest is then performed by using a pursuit algorithm to find their sparse representations with respect to the dictionary. For the challenging baseline problem, a wavelet filter is proposed to filter the smooth and broad components of both the observed spectra and the basis functions in the dictionary. The computation of sparse representation can then be carried out by using the remaining data. Simulation results show the good performance of this wavelet filtering-based strategy in separating the overlapping components between the baselines and the spectra of interest, when no appropriate model function for the baseline is available. Quantifications of in vivo brain MR spectra from tumor patients in different stages of progression demonstrate the effectiveness of the proposed method.

Cardinal series to filter oversampled truncated magnetic resonance signals

Digital low pass filters are routinely used to improve the signal-to-noise ratio of NMR signals, e.g. FID or echoes, when pass band widths of the available analogue filters do not correspond to the spectral width of the signals. Applying digital filters will always necessitate an oversampling of the signal to filter. The digital filters with which the commercial spectrometers are nowadays equipped and most of those known to date from literature were designed to be applied to signals in the time domain. Nevertheless, most of them are aimed at optimising the filtering of signals in the frequency domain and tend to distort them in the time domain, especially when applied to truncated signals. Herein we propose a low pass filter that preserves all the features of the signal in both domains. The method consists in fitting raw NMR data with a finite sum of truncated cardinal sine functions and requires nothing but the signal being a band-limited function. We devised sensible and, in practice, hardly restrictive rules for setting parameters of the filter and applied it to various computer-simulated and experimentally measured truncated data sets to demonstrate its success in filtering both FID and echo signals.

Quantitative metabolic profiles of 2nd and 3rd trimester human amniotic fluid using (1)H HR-MAS spectroscopy

Object

To establish and compare normative metabolite concentrations in 2nd and 3rd trimester human amniotic fluid samples in an effort to reveal metabolic biomarkers of fetal health and development.

Materials and methods

Twenty-one metabolite concentrations were compared between 2nd (15–27 weeks gestation, N = 23) and 3rd (29–39 weeks gestation, N = 27) trimester amniotic fluid samples using ¹H high resolution magic angle spinning (HR-MAS) spectroscopy. Data were acquired using the electronic reference to access in vivo concentrations method and quantified using a modified semi-parametric quantum estimation algorithm modified for high-resolution ex vivo data.

Results

Sixteen of 21 metabolite concentrations differed significantly between 2nd and 3rd trimester groups. Betaine (0.00846±0.00206 mmol/kg vs. 0.0133±0.0058 mmol/kg, P <0.002) and creatinine (0.0124±0.0058 mmol/kg vs. 0.247±0.011 mmol/kg, P <0.001) concentrations increased significantly, while glucose (5.96±1.66 mmol/kg vs. 2.41±1.69 mmol/kg, P <0.001), citrate (0.740±0.217 mmol/kg vs. 0.399±0.137 mmol/kg, P <0.001), pyruvate (0.0659±0.0103 mmol/kg vs. 0.0299±0.286 mmol/kg, P <0.001), and numerous amino acid (e.g. alanine, glutamate, isoleucine, leucine, lysine, and valine) concentrations decreased significantly with advancing gestation. A stepwise multiple linear regression model applied to 50 samples showed that gestational age can be accurately predicted using combinations of alanine, glucose and creatinine concentrations.

Conclusion

These results provide key normative data for 2nd and 3rd trimester amniotic fluid metabolite concentrations and provide the foundation for future development of magnetic resonance spectroscopy (MRS) biomarkers to evaluate fetal health and development.