Time-domain semi-parametric estimation based on a metabolite basis set

Quantification of the neurochemical profile using simulated macromolecule resonances at 3 T

The broad resonances underlying the entire (1) H NMR spectrum of the brain, ascribed to macromolecules, can influence metabolite quantification. At the intermediate field strength of 3 T, distinct approaches for the determination of the macromolecule signal, previously used at either 1.5 or 7 T and higher, may become equivalent. The aim of this study was to evaluate, at 3 T for healthy subjects using LCModel, the impact on the metabolite quantification of two different macromolecule approaches: (i) experimentally measured macromolecules; and (ii) mathematically estimated macromolecules. Although small, but significant, differences in metabolite quantification (up to 23% for glutamate) were noted for some metabolites, 10 metabolites were quantified reproducibly with both approaches with a Cramer-Rao lower bound below 20%, and the neurochemical profiles were therefore similar. We conclude that the mathematical approximation can provide sufficiently accurate and reproducible estimation of the macromolecule contribution to the (1) H spectrum at 3 T.

Magnetic resonance spectroscopy in vivo of neurochemicals in a transgenic model of Alzheimer's disease: a longitudinal study of metabolites, relaxation time, and behavioral analysis in TASTPM and wild-type mice

Alzheimer's disease (AD) is the most common form of dementia in the elderly. Due to ongoing advances in our understanding of the underlying pathology of AD, many potential new targets for therapeutics are becoming available. Transgenic mouse models of AD have helped in furthering our understanding of AD and also provide a vehicle for preclinical testing of new, putative disease-modifying therapeutics, which may have potential for translation to use in clinical trials. To identify possible translational biomarkers, we have studied the longitudinal cerebral metabolic pattern of the TASTPM transgenic AD mouse, a double transgenic mouse overexpressing human mutant amyloid precursor protein (hAPP695swe) and presenilin-1 (M146V) by (1) H magnetic resonance spectroscopy, along with concurrent brain T1 /T2 mapping and behavioral testing. We found significant differences in creatine, glutamate, N-acetylaspartate, choline-containing compounds, and myo-inositol between TASTPM and wild-type mice. In the case of N-acetylaspartate and myo-inositol, there were similarities to differences detected in human AD. T1 /T2 values were shorter overall in TASTPM mice, indicating possible differences in water content between TASTPM and wild-type mice. In older TASTPM mice, exploratory behavior became more random, indicating a possible memory deficiency. The decrease in behavioral performance correlated in the transgenic group with higher expression of myo-inositol.

Altered reward circuitry in the norepinephrine transporter knockout mouse

Synaptic levels of the monoamine neurotransmitters dopamine, serotonin, and norepinephrine are modulated by their respective plasma membrane transporters, albeit with a few exceptions. Monoamine transporters remove monoamines from the synaptic cleft and thus influence the degree and duration of signaling. Abnormal concentrations of these neuronal transmitters are implicated in a number of neurological and psychiatric disorders, including addiction, depression, and attention deficit/hyperactivity disorder. This work concentrates on the norepinephrine transporter (NET), using a battery of in vivo magnetic resonance imaging techniques and histological correlates to probe the effects of genetic deletion of the norepinephrine transporter on brain metabolism, anatomy and functional connectivity. MRS recorded in the striatum of NET knockout mice indicated a lower concentration of NAA that correlates with histological observations of subtle dysmorphisms in the striatum and internal capsule. As with DAT and SERT knockout mice, we detected minimal structural alterations in NET knockout mice by tensor-based morphometric analysis. In contrast, longitudinal imaging after stereotaxic prefrontal cortical injection of manganese, an established neuronal circuitry tracer, revealed that the reward circuit in the NET knockout mouse is biased toward anterior portions of the brain. This is similar to previous results observed for the dopamine transporter (DAT) knockout mouse, but dissimilar from work with serotonin transporter (SERT) knockout mice where Mn²⁺ tracings extended to more posterior structures than in wildtype animals. These observations correlate with behavioral studies indicating that SERT knockout mice display anxiety-like phenotypes, while NET knockouts and to a lesser extent DAT knockout mice display antidepressant-like phenotypic features. Thus, the mainly anterior activity detected with manganese-enhanced MRI in the DAT and NET knockout mice is likely indicative of more robust connectivity in the frontal portion of the reward circuit of the DAT and NET knockout mice compared to the SERT knockout mice.

N-acetylaspartylglutamate (NAAG) and N-acetylaspartate (NAA) in patients with schizophrenia

BACKGROUND : Imbalance of glutamatergic neurotransmission has been proposed as a key mechanism underlying symptoms of schizophrenia. The neuropetide N-acetylaspartylglutamate (NAAG) modulates glutamate release. NAAG provides a component of the proton magnetic resonance spectrum (1H-MRS) in humans. The signal of NAAG, however, largely overlaps with its precursor and degrading product N-acetylaspartate (NAA) that by itself does not act in glutamatergic neurotransmission.

METHODS

We quantified NAAG and NAA separately from the 1H-MRS signal in 20 patients with schizophrenia and 20 healthy comparison subjects on a 3.0 Tesla MR scanner. The 1H-MRS voxels were positioned in the anterior cingulate cortex (ACC) and in the left frontal lobe. Psychopathological symptoms and cognitive performance were assessed.

RESULTS

In the ACC, the ratio NAAG/NAA was increased (P = .041) and NAAG was increased at a trend level (P = .066) in patients, while NAA was reduced (P = .030). NAA correlated with attention performance in patients (r = .64, P = .005) in the ACC. There was no group difference of NAAG, NAA, or NAAG/NAA in the frontal lobe but an inverse correlation of NAAG with negatives symptoms (Positive and Negative Symptoms Scale [PANSS] negative, r = -.58, P = .018) and with the total symptom score (PANSS total, r = -.50, P = .049). In addition, there was a positive correlation of frontal lobe NAAG (r = .53, P = .035) and NAAG/NAA (r = .54, P = .030) with episodic memory in patients.

CONCLUSIONS

In this study, we present the first in vivo evidence for altered NAAG concentration in patients with schizophrenia.

Prediction of pathologic response to neoadjuvant chemotherapy in patients with breast cancer using diffusion-weighted imaging and MRS

The aim of this study was to determine whether tumor size, MRS parameters and apparent diffusion coefficient (ADC) measurements could be applied to predict pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC). Ninety patients with breast cancer (median size, 4.5 cm; range, 1.6-9.5 cm) were evaluated with single-voxel ¹H MRS and dynamic contrast-enhanced MRI. Diffusion-weighted imaging was performed in 41 of these patients using a 1.5-T scanner before and after completion of NAC. Pre- and post-treatment measurements and changes in tumor size, MRS parameters [absolute and normalized total choline-containing compound (tCho) integral and tCho signal-to-noise ratio (SNR)] and ADCs in pCR versus non-pCR were compared using the nonparametric Mann-Whitney test. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic performance of each parameter. After NAC, 30 patients (33%) showed pCR and 60 (67%) showed non-pCR. At pretreatment, ADC was the only significant parameter in differentiating between pCR and non-pCR [(0.83 ± 0.05) × 10⁻³ versus (0.97 ± 0.14) × 10⁻³ mm²/s] (p = 0.014). Post-treatment measurements after completion of NAC and changes in tumor size (both p < 0.001), MRS parameters (p = 0.027 and p = 0.020 for absolute tCho integral, p = 0.036 and p = 0.023 for normalized tCho integral, and p = 0.032 and p = 0.061 for tCho SNR) and ADC (p = 0.003 and p < 0.001) were significantly different between the pCR and non-pCR groups, except for changes in tCho SNR. In ROC analysis, the areas under the ROC curve (AUCs) of 0.63-0.73 were obtained for tumor size and MRS parameters. AUCs for pre- and post-treatment ADC and changes in ADC were 0.75, 0.80 and 0.96, respectively. The optimal cut-off of the percentage change in ADC for predicting pCR was 40.7%, yielding 100% sensitivity and 91% specificity. Patients with pCR showed significantly lower pretreatment ADCs than those with non-pCR. The change in ADC after NAC was the most accurate predictor of pCR.

(1) H magnetic resonance spectroscopy of neurodegeneration in a mouse model of niemann-pick type C1 disease

PURPOSE

To evaluate brain metabolite levels as in vivo indicators of disease progression in a widely studied mouse model of Niemann-Pick type C1 (NPC1) disease with quantitative (1) H magnetic resonance spectroscopy (MRS).

MATERIALS AND METHODS

Single voxel MRS experiments were carried out in vivo in a mouse model of NPC1 disease and in control mice in two brain regions (central and posterior) at two timepoints (presymptomatic and endstage) to examine changes in metabolite levels in NPC1 disease. Concentrations of nine metabolites were quantified by fitting a simulated basis set of metabolite signals to the acquired spectra.

RESULTS

The only differences found in brain metabolite levels between NPC1 disease model and control mice were increased myo-inositol and decreased taurine in the posterior region of the brain at the endstage of the disease. Metabolite changes reported in past clinical MRS studies of NPC disease were not found in the current study of the mouse model.

CONCLUSIONS

The (1) H spectra obtained from NPC1 mice and control mice were very similar, even at endstages of the disease. Although differences in two metabolites associated with neurodegenerative diseases were found and could inform future studies of the disease model, it appears that MRS in this mouse model of NPC1 disease does not have the sensitivity desired for a biomarker.

Optimized quantitative magnetic resonance spectroscopy for clinical routine

Several practical obstacles in data handling and evaluation complicate the use of quantitative localized magnetic resonance spectroscopy (qMRS) in clinical routine MR examinations. To overcome these obstacles, a clinically feasible MR pulse sequence protocol based on standard available MR pulse sequences for qMRS has been implemented along with newly added functionalities to the free software package jMRUI-v5.0 to make qMRS attractive for clinical routine. This enables (a) easy and fast DICOM data transfer from the MR console and the qMRS-computer, (b) visualization of combined MR spectroscopy and imaging, (c) creation and network transfer of spectroscopy reports in DICOM format, (d) integration of advanced water reference models for absolute quantification, and (e) setup of databases containing normal metabolite concentrations of healthy subjects. To demonstrate the work-flow of qMRS using these implementations, databases for normal metabolite concentration in different regions of brain tissue were created using spectroscopic data acquired in 55 normal subjects (age range 6-61 years) using 1.5T and 3T MR systems, and illustrated in one clinical case of typical brain tumor (primitive neuroectodermal tumor). The MR pulse sequence protocol and newly implemented software functionalities facilitate the incorporation of qMRS and reference to normal value metabolite concentration data in daily clinical routine.

Evaluation of breast cancer using proton MR spectroscopy: total choline peak integral and signal-to-noise ratio as prognostic indicators

OBJECTIVE

The purpose of this article is to determine whether the peak integral and signal-to-noise ratio (SNR) of total choline-containing compounds obtained by MR spectroscopy (MRS) correlate with histologic biomarkers currently used for predicting prognosis in patients with breast cancer.

MATERIALS AND METHODS

Single-voxel proton MRS using a 1.5-T scanner was performed in 184 patients (mean age, 48 years; range, 28-72 years) with breast cancer. We obtained absolute total choline-containing compound peak integral, total choline-containing compound peak integral normalized for the volume of interest, and SNR after MRI. On surgical pathology, pathologic subtype and prognostic factors such as nuclear grade, histologic grade, estrogen receptor (ER), HER-2≠neu, extensive intraductal component (EIC), lymphovascular invasion, and lymph node metastasis were also evaluated. Statistical analysis was performed using Mann-Whitney U test and Spearman rank correlation.

RESULTS

The total choline-containing compound SNR, absolute total choline-containing compound peak integral, and normalized total choline-containing compound integral were significantly higher for invasive ductal carcinoma, cancer of high nuclear or histologic grade, and EIC-negative cancer (p < 0.001) than for in situ or other invasive carcinomas (p = 0.005), cancer of low nuclear or histologic grade (p = 0.009), and EIC-positive cancer (p = 0.017). There was a significant difference in the total choline-containing compound SNR between ER-positive and -negative groups (p = 0.007) and between triple-negative and non-triple-negative groups (p = 0.002). A positive correlation was found between the volume of interest (p < 0.001), tumor size (p = 0.011), and three MRS parameters (p = 0.003).

CONCLUSION

Our study suggests that proton MRS can play a role in predicting prognostic indicators of tumor aggressiveness in patients with newly diagnosed breast cancer.

A phase and frequency alignment protocol for 1H MRSI data of the prostate

(1)H MRSI of the prostate reveals relative metabolite levels that vary according to the presence or absence of tumour, providing a sensitive method for the identification of patients with cancer. Current interpretations of prostate data rely on quantification algorithms that fit model metabolite resonances to individual voxel spectra and calculate relative levels of metabolites, such as choline, creatine, citrate and polyamines. Statistical pattern recognition techniques can potentially improve the detection of prostate cancer, but these analyses are hampered by artefacts and sources of noise in the data, such as variations in phase and frequency of resonances. Phase and frequency variations may arise as a result of spatial field gradients or local physiological conditions affecting the frequency of resonances, in particular those of citrate. Thus, there are unique challenges in developing a peak alignment algorithm for these data. We have developed a frequency and phase correction algorithm for automatic alignment of the resonances in prostate MRSI spectra. We demonstrate, with a simulated dataset, that alignment can be achieved to a phase standard deviation of 0.095  rad and a frequency standard deviation of 0.68  Hz for the citrate resonances. Three parameters were used to assess the improvement in peak alignment in the MRSI data of five patients: the percentage of variance in all MRSI spectra explained by their first principal component; the signal-to-noise ratio of a spectrum formed by taking the median value of the entire set at each spectral point; and the mean cross-correlation between all pairs of spectra. These parameters showed a greater similarity between spectra in all five datasets and the simulated data, demonstrating improved alignment for phase and frequency in these spectra. This peak alignment program is expected to improve pattern recognition significantly, enabling accurate detection and localisation of prostate cancer with MRSI.

Multimodal wavelet embedding representation for data combination (MaWERiC): integrating magnetic resonance imaging and spectroscopy for prostate cancer detection

Recently, both Magnetic Resonance (MR) Imaging (MRI) and Spectroscopy (MRS) have emerged as promising tools for detection of prostate cancer (CaP). However, due to the inherent dimensionality differences in MR imaging and spectral information, quantitative integration of T(2) weighted MRI (T(2)w MRI) and MRS for improved CaP detection has been a major challenge. In this paper, we present a novel computerized decision support system called multimodal wavelet embedding representation for data combination (MaWERiC) that employs, (i) wavelet theory to extract 171 Haar wavelet features from MRS and 54 Gabor features from T(2)w MRI, (ii) dimensionality reduction to individually project wavelet features from MRS and T(2)w MRI into a common reduced Eigen vector space, and (iii), a random forest classifier for automated prostate cancer detection on a per voxel basis from combined 1.5 T in vivo MRI and MRS. A total of 36 1.5 T endorectal in vivo T(2)w MRI and MRS patient studies were evaluated per voxel by MaWERiC using a three-fold cross validation approach over 25 iterations. Ground truth for evaluation of results was obtained by an expert radiologist annotations of prostate cancer on a per voxel basis who compared each MRI section with corresponding ex vivo wholemount histology sections with the disease extent mapped out on histology. Results suggest that MaWERiC based MRS T(2)w meta-classifier (mean AUC, μ = 0.89 ± 0.02) significantly outperformed (i) a T(2)w MRI (using wavelet texture features) classifier (μ = 0.55 ± 0.02), (ii) a MRS (using metabolite ratios) classifier (μ = 0.77 ± 0.03), (iii) a decision fusion classifier obtained by combining individual T(2)w MRI and MRS classifier outputs (μ = 0.85 ± 0.03), and (iv) a data combination method involving a combination of metabolic MRS and MR signal intensity features (μ = 0.66 ± 0.02).

A hybrid method of application of independent component analysis to in vivo 1H MR spectra of childhood brain tumours

Independent component analysis (ICA) can automatically extract individual metabolite, macromolecular and lipid (MMLip) components from a series of in vivo MR spectra. The traditional feature extraction (FE)-based ICA approach is limited, in that a large sample size is required and a combination of metabolite and MMLip components can appear in the same independent component. The alternative ICA approach, based on blind source separation (BSS), is weak when dealing with overlapping peaks. Combining the advantages of both BSS and FE methods may lead to better results. Thus, we propose an ICA approach involving a hybrid of the BSS and FE techniques for the automated decomposition of a series of MR spectra. Experiments were performed on synthesised and patient in vivo childhood brain tumour MR spectra datasets. The hybrid ICA method showed an improvement in the decomposition ability compared with BSS-ICA or FE-ICA, with an increased correlation between the independent components and simulated metabolite and MMLip signals. Furthermore, we were able to automatically extract metabolites from the patient MR spectra dataset that were not in commonly used basis sets (e.g. guanidinoacetate).

Correlation of diffusion and metabolic alterations in different clinical forms of multiple sclerosis

Diffusion tensor imaging (DTI) and MR spectroscopic imaging (MRSI) provide greater sensitivity than conventional MRI to detect diffuse alterations in normal appearing white matter (NAWM) of Multiple Sclerosis (MS) patients with different clinical forms. Therefore, the goal of this study is to combine DTI and MRSI measurements to analyze the relation between diffusion and metabolic markers, T2-weighted lesion load (T2-LL) and the patients clinical status. The sensitivity and specificity of both methods were then compared in terms of MS clinical forms differentiation. MR examination was performed on 71 MS patients (27 relapsing remitting (RR), 26 secondary progressive (SP) and 18 primary progressive (PP)) and 24 control subjects. DTI and MRSI measurements were obtained from two identical regions of interest selected in left and right centrum semioval (CSO) WM. DTI metrics and metabolic contents were significantly altered in MS patients with the exception of N-acetyl-aspartate (NAA) and NAA/Choline (Cho) ratio in RR patients. Significant correlations were observed between diffusion and metabolic measures to various degrees in every MS patients group. Most DTI metrics were significantly correlated with the T2-LL while only NAA/Cr ratio was correlated in RR patients. A comparison analysis of MR methods efficiency demonstrated a better sensitivity/specificity of DTI over MRSI. Nevertheless, NAA/Cr ratio could distinguish all MS and SP patients groups from controls, while NAA/Cho ratio differentiated PP patients from controls. This study demonstrated that diffusivity changes related to microstructural alterations were correlated with metabolic changes and provided a better sensitivity to detect early changes, particularly in RR patients who are more subject to inflammatory processes. In contrast, the better specificity of metabolic ratios to detect axonal damage and demyelination may provide a better index for identification of PP patients.

Using spatial prior knowledge in the spectral fitting of MRS images

We propose a Bayesian smoothness prior in the spectral fitting of MRS images which can be used in addition to commonly employed prior knowledge. By combining a frequency-domain model for the free induction decay with a Gaussian Markov random field prior, a new optimization objective is derived that encourages smooth parameter maps. Using a particular parameterization of the prior, smooth damping, frequency and phase maps can be obtained whilst preserving sharp spatial features in the amplitude map. A Monte Carlo study based on two sets of simulated data demonstrates that the variance of the estimated parameter maps can be reduced considerably, even below the Cramér-Rao lower bound, when using spatial prior knowledge. Long-TE (1)H MRSI at 1.5 T of a patient with a brain tumor shows that the use of the spatial prior resolves the overlapping peaks of choline and creatine when a single voxel method fails to do so. Improved and detailed metabolic maps can be derived from high-spatial-resolution, short-TE (1)H MRSI at 3 T. Finally, the evaluation of four series of long-TE brain MRSI data with various signal-to-noise ratios shows the general benefit of the proposed approach.

Quantitative proton magnetic resonance spectroscopy and spectroscopic imaging of the brain: a didactic review

This article presents background information related to methodology for estimating brain metabolite concentration from magnetic resonance spectroscopy (MRS) and magnetic resonance spectroscopic imaging measurements of living human brain tissue. It reviews progress related to this methodology, with emphasis placed on progress reported during the past 10 years. It is written for a target audience composed of radiologists and magnetic resonance imaging technologists. It describes in general terms the relationship between MRS signal amplitude and concentration. It then presents an overview of the many practical problems associated with deriving concentration solely from absolute measured signal amplitudes and demonstrates how a various signal calibration approaches can be successfully used. The concept of integrated signal amplitude is presented with examples that are helpful for qualitative reading of MRS data as well as for understanding the methodology used for quantitative measurements. The problems associated with the accurate measurement of individual signal amplitudes in brain spectra having overlapping signals from other metabolites and overlapping nuisance signals from water and lipid are presented. Current approaches to obtaining accurate amplitude estimates with least-squares fitting software are summarized.

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.

Exploiting spatial information to estimate metabolite levels in two-dimensional MRSI of heterogeneous brain lesions

MRSI provides MR spectra from multiple adjacent voxels within a body volume represented as a two- or three-dimensional matrix, allowing the measurement of the distribution of metabolites over this volume. The spectra of these voxels are usually analyzed one by one, without exploiting their spatial context. In this article, we present an advanced metabolite quantification method for MRSI data, in which the available spatial information is considered. A nonlinear least-squares algorithm is proposed in which prior knowledge is included in the form of proximity constraints on the spectral parameters within a grid and optimized starting values. A penalty term that promotes a spatially smooth spectral parameter map is added to the fitting algorithm. This method is adaptive, in the sense that several sweeps through the grid are performed and each solution may tune some hyperparameters at run-time. Simulation studies of MRSI data showed significantly improved metabolite estimates after the inclusion of spatial information. Improved metabolite maps were also demonstrated by applying the method to in vivo MRSI data. Overlapping peaks or peaks of compounds present at low concentration can be better quantified with the proposed method than with single-voxel approaches. The new approach compares favorably against the multivoxel approach embedded in the well-known quantification software LCModel.

Implementation of an absolute brain 1H-MRS quantification method to assess different tissue alterations in multiple sclerosis

Magnetic resonance spectroscopy has emerged as a sensitive modality to detect early and diffuse alterations in multiple sclerosis. Recently, the hypothesis of neurodegenerative pathogenesis has highlightened the interest for measurement of metabolites concentrations, to gain specificity, in a large brain volume encompassing different tissue alterations. Therefore, we proposed in this paper the implementation of an absolute quantification method based on localized spectroscopy at short (30 ms) and long (135 ms) echo time of a volume including normal appearing white matter, cortical gray matter, and lesions. First, methodological developments were implemented including external calibration, and corrections of phased-array coil sensitivity and cerebrospinal fluid volume contribution. Second, these improvements were validated and optimized using an original methodology based on simulations of brain images with lesions. Finally, metabolic alterations were assessed in 65 patients including 26 relapsing-remitting, 17 primary-progressive (PP), 22 secondary-progressive (SP) patients, and in 23 normal subjects. Results showed increases of choline, creatine, and myo-inositol concentrations in PP and SP patients compared to controls, whereas the concentration of N-acetyl compounds remained constant. The major finding of this study was the identification of Cho concentration and Cho/tNA ratio as putative markers of progressive onset, suggesting interesting perspectives in detection and followup of neurodegenerative processes.

Does visual cortex lactate increase following photic stimulation in migraine without aura patients? A functional (1)H-MRS study

Proton magnetic resonance spectroscopy ((1)H-MRS) has been used in a number of studies to assess noninvasively the temporal changes of lactate (Lac) in the activated human brain. Migraine neurobiology involves lack of cortical habituation to repetitive stimuli and a mitochondrial component has been put forward. Our group has recently demonstrated a reduction in the high-energy phosphates adenosine triphosphate (ATP) and phosphocreatine (PCr) in the occipital lobe of migraine without aura (MwoA) patients, at least in a subgroup, in a phosphorus MRS ((31)P-MRS) study. In previous studies, basal Lac levels or photic stimulation (PS)-induced Lac levels were found to be increased in patients with migraine with aura (MwA) and migraine patients with visual symptoms and paraesthesia, paresia and/or dysphasia, respectively. The aim of this study was to perform functional (1)H-MRS at 3 T in 20 MwoA patients and 20 control subjects. Repetitive visual stimulation was applied using MR-compatible goggles with 8 Hz checkerboard stimulation during 12 min. We did not observe any significant differences in signal integrals, ratios and absolute metabolite concentrations, including Lac, between MwoA patients and controls before PS. Lac also did not increase significantly during and following PS, both for MwoA patients and controls. Subtle Lac changes, smaller than the sensitivity threshold (i.e. estimated at 0.1-0.2 μmol/g at 3 T), cannot be detected by MRS. Our study does, however, argue against a significant switch to non-aerobic glucose metabolism during long-lasting PS of the visual cortex in MwoA patients.

Loss of neuronal integrity during progressive HIV-1 infection of humanized mice

Neuronal damage induced by ongoing human immunodeficiency virus type 1 (HIV-1) infection was investigated in humanized NOD/scid-IL-2Rγ(c)(null) mice transplanted at birth with human CD34-positive hematopoietic stem cells. Mice infected at 5 months of age and followed for up to 15 weeks maintained significant plasma viral loads and showed reduced numbers of CD4(+) T-cells. Prospective serial proton magnetic resonance spectroscopy tests showed selective reductions in cortical N-acetyl aspartate in infected animals. Diffusion tensor imaging revealed structural changes in cortical gray matter. Postmortem immunofluorescence brain tissue examinations for neuronal and glial markers, captured by multispectral imaging microscopy and quantified by morphometric and fluorescence emission, showed regional reduction of neuronal soma and synaptic architectures. This was evidenced by loss of microtubule-associated protein 2, synaptophysin, and neurofilament antigens. This study is the first, to our knowledge, demonstrating lost neuronal integrity after HIV-1 infection in humanized mice. As such, the model permits studies of the relationships between ongoing viral replication and virus-associated neurodegeneration.

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.

Influence of brain tumors on the MR spectra of healthy brain tissue

The neurochemical environment of nontumorous white matter tissue was investigated in 135 single voxel spectra of "healthy" white matter regions of 43 tumor patients and 129 spectra of 52 healthy subjects. Spectra were acquired with short TE and TR values. With the data of tumor patients, it was examined whether differences were caused by the tumor itself or aggressive tumor therapies as confounding factors. Comparing the spectra of both classes, an excellent differentiation was possible based on the metabolite peak of N-acetylaspartate (P ≈ 0) and myoinositol (P < 0.03). The area under curve of the receiver operating characteristic was calculated as 0.86 and 0.62, respectively. With linear discriminant analysis using combinations of integrals, a prediction was possible, whether a spectrum belonged to the patient or the healthy subject class with an overall accuracy above 80%. The confounding factors could be ruled out as source of the differences. The results show strong evidence for an influence of malignant growth on the biochemical environment of nontumorous white matter tissue. Because of the T(1) weighting, the measured differences between both classes were most likely concentration changes interfered by T(1) effects. The underlying processes will be subject of future studies.

Quantification of high-resolution (1)H NMR spectra from rat brain extracts

Extracting quantitative information about absolute concentrations from high-resolution (1)H NMR spectra of complex mixtures such as brain extracts remains challenging. Partial overlap of resonances complicates integration, whereas simple line fitting algorithms cannot accommodate the spectral complexity of coupled spin systems. Here, it is shown that high-resolution (1)H NMR spectra of rat brain extracts from 11 distinct brain regions can be reproducibly quantified using a basis set of 29 compounds. The basis set is simulated with the density matrix formalism using complete prior knowledge of chemical shifts and scalar couplings. A crucial aspect to obtain reproducible results was the inclusion of a line shape distortion common among all 73 resonances of the 29 compounds. All metabolites could be quantified with <10% and <3% inter- and intrasubject variation, respectively.

Registered bioimaging of nanomaterials for diagnostic and therapeutic monitoring

Nanomedications can be carried by blood borne monocyte-macrophages into the reticuloendothelial system (RES; spleen, liver, lymph nodes) and to end organs. The latter include the lung, RES, and brain and are operative during human immunodeficiency virus type one (HIV-1) infection. Macrophage entry into tissues is notable in areas of active HIV-1 replication and sites of inflammation. In order to assess the potential of macrophages as nanocarriers, superparamagnetic iron-oxide and/or drug laden particles coated with surfactants were parenterally injected into HIV-1 encephalitic mice. This was done to quantitatively assess particle and drug biodistribution. Magnetic resonance imaging (MRI) test results were validated by histological coregistration and enhanced image processing. End organ disease as typified by altered brain histology were assessed by MRI. The demonstration of robust migration of nanoformulations into areas of focal encephalitis provides '"proof of concept" for the use of advanced bioimaging techniques to monitor macrophage migration. Importantly, histopathological aberrations in brain correlate with bioimaging parameters making the general utility of MRI in studies of cell distribution in disease feasible. We posit that using such methods can provide a real time index of disease burden and therapeutic efficacy with translational potential to humans.

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%.

A comparison between simulated and experimental basis sets for assessing short-TE in vivo ¹H MRS data at 1.5 T

A number of algorithms designed to determine metabolite concentrations from in vivo (1)H MRS require a collection of single metabolite spectra, known as a basis set, which can be obtained experimentally or by simulation. It has been assumed that basis sets can be used interchangeably, but no systematic study has investigated the effects of small variations in basis functions on the metabolite values obtained. The aim of this study was to compare the results of simulated with experimental basis sets when used to fit short-TE (1)H MRS data of variable quality at 1.5 T. Two hundred and twelve paediatric brain tumour spectra were included in the analysis, and each was analysed twice with LCModel™ using a simulated and experimental basis set. To determine the influence of data quality on quantification, each spectrum was assessed and 152 were classified as being of 'good' quality. Bland-Altman statistics were used to measure the agreement between the two basis sets for all available spectra and only 'good'-quality spectra. Monte-Carlo simulations were performed to investigate the influence of minor shifts in metabolite frequencies on metabolite concentration estimates. All metabolites showed good agreement between the two basis sets, and the average metabolite limits of agreement were approximately ±3.84 mM for all available data and ±0.99 mM for good-quality data. Errors obtained from the Monte-Carlo analysis were found to be more accurate than the Cramer-Rao lower bounds (CRLB) for 12 of 15 metabolites when metabolite frequency shifting was considered. For the majority of purposes, a level of agreement of ±0.99 mM between simulated and experimental basis sets is sufficiently small for them to be used interchangeably. Multiple analyses using slightly modified basis sets may be useful in estimating fitting errors, which are not predicted by CRLBs.

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.

Ex vivo MR spectroscopic measure differentiates tumor from treatment effects in GBM

The motivation of this study was to address the urgent clinical problem related to the inability of magnetic resonance (MR) imaging measures to differentiate tumor progression from treatment effects in patients with glioblastoma multiforme (GBM). While contrast enhancement on MR imaging (MRI) is routinely used for assessment of tumor burden, therapy response, and progression-free survival in GBM, it is well known that changes in enhancement following treatment are nonspecific to tumor. To address this issue, the objective of this study was to investigate whether MR spectroscopy can provide improved biomarker surrogates for tumor following treatment. High-resolution metabolic profiles of tissue samples obtained from patients with GBM were directly correlated with their pathological assessment to determine metabolic markers that correspond to pathological indications of tumor or treatment effects. Acquisition of tissue samples with image guidance enabled the association of ex vivo biochemical and pathological properties of the tissue samples with in vivo MR anatomical and structural properties derived from presurgical MR images. Using this approach, we found that metabolic concentration levels of [Myo-inositol/total choline (MCI)] in tissue samples are able to differentiate tumor from nontumor and treatment-induced reactive astrocytosis with high significance (P < .001) in newly diagnosed and recurrent GBM. The MCI index has a sensitivity of 93% to tumor in recurrent GBM and delineates the contribution of cellularity that originates from tumor and astrocytic proliferation following treatment. Low levels of MCI for tumor were associated with a reduced apparent diffusion coefficient and elevated choline-N-acetyl-aspartate index derived from in vivo MR images.

Altered neurocircuitry in the dopamine transporter knockout mouse brain

The plasma membrane transporters for the monoamine neurotransmitters dopamine, serotonin, and norepinephrine modulate the dynamics of these monoamine neurotransmitters. Thus, activity of these transporters has significant consequences for monoamine activity throughout the brain and for a number of neurological and psychiatric disorders. Gene knockout (KO) mice that reduce or eliminate expression of each of these monoamine transporters have provided a wealth of new information about the function of these proteins at molecular, physiological and behavioral levels. In the present work we use the unique properties of magnetic resonance imaging (MRI) to probe the effects of altered dopaminergic dynamics on meso-scale neuronal circuitry and overall brain morphology, since changes at these levels of organization might help to account for some of the extensive pharmacological and behavioral differences observed in dopamine transporter (DAT) KO mice. Despite the smaller size of these animals, voxel-wise statistical comparison of high resolution structural MR images indicated little morphological change as a consequence of DAT KO. Likewise, proton magnetic resonance spectra recorded in the striatum indicated no significant changes in detectable metabolite concentrations between DAT KO and wild-type (WT) mice. In contrast, alterations in the circuitry from the prefrontal cortex to the mesocortical limbic system, an important brain component intimately tied to function of mesolimbic/mesocortical dopamine reward pathways, were revealed by manganese-enhanced MRI (MEMRI). Analysis of co-registered MEMRI images taken over the 26 hours after introduction of Mn²⁺ into the prefrontal cortex indicated that DAT KO mice have a truncated Mn²⁺ distribution within this circuitry with little accumulation beyond the thalamus or contralateral to the injection site. By contrast, WT littermates exhibit Mn²⁺ transport into more posterior midbrain nuclei and contralateral mesolimbic structures at 26 hr post-injection. Thus, DAT KO mice appear, at this level of anatomic resolution, to have preserved cortico-striatal-thalamic connectivity but diminished robustness of reward-modulating circuitry distal to the thalamus. This is in contradistinction to the state of this circuitry in serotonin transporter KO mice where we observed more robust connectivity in more posterior brain regions using methods identical to those employed here.

Calculation of gestational age in late second and third trimesters by ex vivo magnetic resonance spectroscopy of amniotic fluid

OBJECTIVE

The purpose of this study was to develop a reliable model for the calculation of gestational age (GA) in second and third trimesters with the use of amniotic fluid (AF) metabolite profiles that were determined by magnetic resonance spectroscopy.

STUDY DESIGN

High-resolution (11.7 T) ex vivo magnetic resonance spectroscopy was performed on 95 AF samples (mean, 31.7 weeks; range, 15.6-39.9 weeks). GA was determined by last menstrual period or first-trimester ultrasound scanning. Concentrations of 21 AF metabolites were measured with automated techniques. Metabolite concentrations, inverses, natural logs, and squares were entered as predictive variables in a stepwise linear regression model.

RESULTS

The following formula was derived: GA = 64.922 - (14.456 x alanine) + (4.965 x natural log [creatinine]) - (0.931 x glucose) - (5.202 x valine). This model fit the data with an R(2) value of 0.926. Average error among all samples was +/-1.75 weeks (SD, +/-1.43 weeks), for the second trimester was +/-2.21 weeks (SD, +/-1.78 weeks), and for the third trimester was +/-1.59 weeks (SD, +/-1.26 weeks).

CONCLUSION

Statistical modeling accurately predicted GA with amniotic fluid metabolite profiles that were obtained by magnetic resonance spectroscopy, which may represent a significant improvement over conventional ultrasound dating in the third trimester. Future studies should compare these techniques directly.

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.

A novel application of 1H magnetic resonance spectroscopy: non-invasive identification of spermatogenesis in men with non-obstructive azoospermia

BACKGROUND

About 10% of infertile men have no sperm in their ejaculate due to poor or absent spermatogenesis, also known as non-obstructive azoospermia (NOA). Testis (1)H magnetic resonance spectroscopy ((1)H-MRS) is a non-invasive imaging tool that can potentially identify and localize spermatogenesis in the testis. This study sought to identify metabolic signatures associated with various histological states of spermatogenesis in infertile men.

METHODS

Quantitative high resolution magic angle spinning spectroscopy was performed on snap frozen testicular tissue from 27 men with three classic histological patterns: (i) normal spermatogenesis (men with prior paternity undergoing vasectomy reversal), (ii) maturation arrest (early or late, MA) or (iii) Sertoli-cell only (SCO). Concentrations of 19 tissue metabolites were acquired from each biopsy specimen. One-way ANOVA analysis was used to determine inter-group differences in metabolite concentrations among the three histologic groups.

RESULTS

Phosphocholine (PC) and taurine tissue concentrations were significantly different between normal and SCO tissue. Mean PC concentrations were three times higher in normal testes compared with SCO (5.4 +/- 1.4 versus 1.5 +/- 0.3 mmol/kg; P = 0.01). No differences in metabolite concentrations were observed between normal and MA testes or between SCO and MA testes. Further histologic stratification of MA testes into subsets of those with (early) and without (late) spermatids or mature sperm, identified differences in PC concentrations. A predictive model for sperm presence with (1)H-MRS was developed based upon PC tissue concentrations.

CONCLUSIONS

PC concentrations are significantly higher in testes with spermatogenesis. This suggests that a unique metabolic signature for spermatogenesis is possible using (1)H-MRS which could aid in the non-invasive diagnosis of sperm in men with NOA.

Normal metabolite levels in the left dorsolateral prefrontal cortex of unmedicated major depressive disorder patients: a single voxel (1)H spectroscopy study

Few proton magnetic resonance spectroscopy ((1)H spectroscopy) studies have investigated the dorsolateral prefrontal cortex (DLPFC), a key region in the pathophysiology of major depressive disorder (MDD). We used (1)H spectroscopy to verify whether MDD patients differ from healthy controls (HC) in metabolite levels in this brain area. Thirty-seven unmedicated DSM-IV MDD patients were compared with 40 HC. Subjects underwent a short echo-time (1)H spectroscopy examination at 1.5 T, with an 8-cm(3) single voxel placed in the left DLPFC. Reliable absolute metabolite levels of N-acetyl aspartate (NAA), phosphocreatine plus creatine (PCr+Cr), choline-containing compounds (GPC+PC), myo-inositol, glutamate plus glutamine (Glu+Gln), and glutamate were obtained using the unsuppressed water signal as an internal reference. Metabolite levels in the left DLPFC did not statistically differ between MDD patients and HC. We found an interaction between gender and diagnosis on PCr+Cr levels. Male MDD patients presented lower levels of PCr+Cr than male HC, and female MDD patients presented higher levels of PCr+Cr than female HC. Moreover, length of illness was inversely correlated with NAA levels. These findings suggest that there is not an effect of diagnosis on the left DLPFC neurochemistry. Possible effects of gender on PCr+Cr levels of MDD patients need to be further investigated.

Pitfalls and advantages of different strategies for the absolute quantification of N-acetyl aspartate, creatine and choline in white and grey matter by 1H-MRS

This study extensively investigates different strategies for the absolute quantitation of N-acetyl aspartate, creatine and choline in white and grey matter by (1)H-MRS at 1.5 T. The main focus of this study was to reliably estimate metabolite concentrations while reducing the scan time, which remains as one of the main problems in clinical MRS. Absolute quantitation was based on the water-unsuppressed concentration as the internal standard. We compared strategies based on various experimental protocols and post-processing strategies. Data were obtained from 30 control subjects using a PRESS sequence at several TE to estimate the transverse relaxation time, T(2), of the metabolites. Quantitation was performed with the algorithm QUEST using two different metabolite signal basis sets: a whole-metabolite basis set (WhoM) and a basis set in which the singlet signals were split from the coupled signals (MSM). The basis sets were simulated in vivo for each TE used. Metabolites' T(2)s were then determined by fitting the estimated signal amplitudes of the metabolites obtained at different TEs. Then the absolute concentrations (mM) of the metabolites were assessed for each subject using the estimated signal amplitudes and either the mean estimated relaxation times of all subjects (mean protocol, MP) or the T(2) estimated from the spectra derived from the same subject (individual protocol, IP). Results showed that MP represents a less time-consuming alternative to IP in the quantitation of brain metabolites by (1)H-MRS in both grey and white matter, with a comparable accuracy when performed by MSM. It was also shown that the acquisition time might be further reduced by using a variant of MP, although with reduced accuracy. In this variant, only one water-suppressed and one water-unsuppressed spectra were acquired, drastically reducing the duration of the entire MRS examination. However, statistical analysis highlights the reduced accuracy of MP when performed using WhoM, particularly at longer echo times.

A hierarchical spectral clustering and nonlinear dimensionality reduction scheme for detection of prostate cancer from magnetic resonance spectroscopy (MRS)

Magnetic resonance spectroscopy (MRS) has been shown to have great clinical potential as a supplement to magnetic resonance imaging in the detection of prostate cancer (CaP). MRS provides functional information in the form of changes in the relative concentration of specific metabolites including choline, creatine, and citrate which can be used to identify potential areas of CaP. With a view to assisting radiologists in interpretation and analysis of MRS data, some researchers have begun to develop computer-aided detection (CAD) schemes for CaP identification from spectroscopy. Most of these schemes have been centered on identifying and integrating the area under metabolite peaks which is then used to compute relative metabolite ratios. However, manual identification of metabolite peaks on the MR spectra, and especially via CAD, is a challenging problem due to low signal-to-noise ratio, baseline irregularity, peak overlap, and peak distortion. In this article the authors present a novel CAD scheme that integrates nonlinear dimensionality reduction (NLDR) with an unsupervised hierarchical clustering algorithm to automatically identify suspicious regions on the prostate using MRS and hence avoids the need to explicitly identify metabolite peaks. The methodology comprises two stages. In stage 1, a hierarchical spectral clustering algorithm is used to distinguish between extracapsular and prostatic spectra in order to localize the region of interest (ROI) corresponding to the prostate. Once the prostate ROI is localized, in stage 2, a NLDR scheme, in conjunction with a replicated clustering algorithm, is used to automatically discriminate between three classes of spectra (normal appearing, suspicious appearing, and indeterminate). The methodology was quantitatively and qualitatively evaluated on a total of 18 1.5 T in vivo prostate T2-weighted (w) and MRS studies obtained from the multisite, multi-institutional American College of Radiology (ACRIN) trial. In the absence of the precise ground truth for CaP extent on the MR imaging for most of the ACRIN studies, probabilistic quantitative metrics were defined based on partial knowledge on the quadrant location and size of the tumor. The scheme, when evaluated against this partial ground truth, was found to have a CaP detection sensitivity of 89.33% and specificity of 79.79%. The results obtained from randomized threefold and fivefold cross validation suggest that the NLDR based clustering scheme has a higher CaP detection accuracy compared to such commonly used MRS analysis schemes as z score and PCA. In addition, the scheme was found to be robust to changes in system parameters. For 6 of the 18 studies an expert radiologist laboriously labeled each of the individual spectra according to a five point scale, with 1/2 representing spectra that the expert considered normal and 3/4/5 being spectra the expert deemed suspicious. When evaluated on these expert annotated datasets, the CAD system yielded an average sensitivity (cluster corresponding to suspicious spectra being identified as the CaP class) and specificity of 81.39% and 64.71%, respectively.

Grid-free interactive and automated data processing for MR chemical shift imaging data

PURPOSE

Today's available chemical shift imaging (CSI) analysis tools are based on Fourier transform of the entire data set prior to interactive display. This strategy is associated with limitations particularly when arbitrary voxel positions within a 3D spatial volume are needed by the user. In this work, we propose and demonstrate a processing-resource-efficient alternative strategy for both interactive and automated CSI data processing up to three spatial dimensions.

METHODS

This approach uses real-time voxel-shift by first-order phase manipulation as a basis and therefore allows grid-free voxel positioning within the 3D volume. The corresponding spectrum is extracted from the 4D data (3D spatial/1D spectral) at each time a voxel position is selected. The spatial response function and hence the exact voxel size and shape are calculated in parallel including the same processing parameters. Using this mechanism sequentially along with AMARES time-domain modeling, we also implemented automated quantitative and B (0)-insensitive metabolite mapping.

RESULTS

Metabolite maps of N-acetyl aspartate, choline and creatine were generated using (1)H-CSI data from the brain of healthy volunteers and patients with tumor and epilepsy. (31)P-3D-CSI of the heart of a healthy volunteer is also shown.

CONCLUSION

The calculated metabolite maps demonstrate good stability and accuracy of the algorithm in all situations tested. The suggested algorithm constitutes therefore an attractive alternative to existing CSI processing strategies.

In vivo neurochemical profiling of rat brain by 1H-[13C] NMR spectroscopy: cerebral energetics and glutamatergic/GABAergic neurotransmission

The quantification of excitatory and inhibitory neurotransmission and the associated energy metabolism is crucial for a proper understanding of brain function. Although the detection of glutamatergic neurotransmission in vivo by (13)C NMR spectroscopy is now relatively routine, the detection of GABAergic neurotransmission in vivo has remained elusive because of the low GABA concentration and spectral overlap. Using (1)H-[(13)C] NMR spectroscopy at high magnetic field in combination with robust spectral modeling and the use of different substrates, [U-(13)C(6)]-glucose and [2-(13)C]-acetate, it is shown that GABAergic, as well as glutamatergic neurotransmitter fluxes can be detected non-invasively in rat brain in vivo.

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.