Application of multiple inversion recovery for suppression of macromolecule resonances in short echo time (1)H NMR spectroscopy of human brain

Diffusion-weighted MR spectroscopy: Consensus, recommendations, and resources from acquisition to modeling

Brain cell structure and function reflect neurodevelopment, plasticity, and aging; and changes can help flag pathological processes such as neurodegeneration and neuroinflammation. Accurate and quantitative methods to noninvasively disentangle cellular structural features are needed and are a substantial focus of brain research. Diffusion-weighted MRS (dMRS) gives access to diffusion properties of endogenous intracellular brain metabolites that are preferentially located inside specific brain cell populations. Despite its great potential, dMRS remains a challenging technique on all levels: from the data acquisition to the analysis, quantification, modeling, and interpretation of results. These challenges were the motivation behind the organization of the Lorentz Center workshop on "Best Practices & Tools for Diffusion MR Spectroscopy" held in Leiden, the Netherlands, in September 2021. During the workshop, the dMRS community established a set of recommendations to execute robust dMRS studies. This paper provides a description of the steps needed for acquiring, processing, fitting, and modeling dMRS data, and provides links to useful resources.

Quantification of short echo time MRS signals with improved version of QUantitation based on quantum ESTimation algorithm

Magnetic resonance spectroscopy offers information about metabolite changes in the organism, which can be used in diagnosis. While short echo time proton spectra exhibit more distinguishable metabolites compared with proton spectra acquired with long echo times, their quantification (and providing estimates of metabolite concentrations) is more challenging. They are hampered by a background signal, which originates mainly from macromolecules (MM) and mobile lipids. An improved version of the quantification algorithm QUantitation based on quantum ESTimation (QUEST), with MM prior knowledge (QUEST-MM), dedicated to proton signals and invoking appropriate prior knowledge on MM, is proposed and tested. From a single acquisition, it enables better metabolite quantification, automatic estimation of the background, and additional automatic quantification of MM components, thus improving its applicability in the clinic. The proposed algorithm may facilitate studies that involve patients with pathological MM in the brain. QUEST-MM and three QUEST-based strategies for quantifying short echo time signals are compared in terms of bias-variance trade-off and Cramér-Rao lower bound estimates. The performances of the methods are evaluated through extensive Monte Carlo studies. In particular, the histograms of the metabolite and MM amplitude distributions demonstrate the performances of the estimators. They showed that QUEST-MM works better than QUEST (Subtract approach) and is a good alternative to QUEST when measured MM signal is unavailable or unsuitable. Quantification with QUEST-MM is shown for 1 H in vivo rat brain signals obtained with the SPECIAL pulse sequence at 9.4 T, and human brain signals obtained, respectively, with STEAM at 4 T and PRESS at 3 T. QUEST-MM is implemented in jMRUI and will be available for public use from version 7.1.

Feasibility and implications of using subject-specific macromolecular spectra to model short echo time magnetic resonance spectroscopy data

Expert consensus recommends linear-combination modeling (LCM) of 1 H MR spectra with sequence-specific simulated metabolite basis function and experimentally derived macromolecular (MM) basis functions. Measured MM basis functions are usually derived from metabolite-nulled spectra averaged across a small cohort. The use of subject-specific instead of cohort-averaged measured MM basis functions has not been studied widely. Furthermore, measured MM basis functions are not widely available to non-expert users, who commonly rely on parameterized MM signals internally simulated by LCM software. To investigate the impact of the choice of MM modeling, this study, therefore, compares metabolite level estimates between different MM modeling strategies (cohort-mean measured; subject-specific measured; parameterized) in a lifespan cohort and characterizes its impact on metabolite-age associations. 100 conventional (TE = 30 ms) and metabolite-nulled (TI = 650 ms) PRESS datasets, acquired from the medial parietal lobe in a lifespan cohort (20-70 years of age), were analyzed in Osprey. Short-TE spectra were modeled in Osprey using six different strategies to consider the MM baseline. Fully tissue- and relaxation-corrected metabolite levels were compared between MM strategies. Model performance was evaluated by model residuals, the Akaike information criterion (AIC), and the impact on metabolite-age associations. The choice of MM strategy had a significant impact on the mean metabolite level estimates and no major impact on variance. Correlation analysis revealed moderate-to-strong agreement between different MM strategies (r > 0.6). The lowest relative model residuals and AIC values were found for the cohort-mean measured MM. Metabolite-age associations were consistently found for two major singlet signals (total creatine (tCr])and total choline (tCho)) for all MM strategies; however, findings for metabolites that are less distinguishable from the background signals associations depended on the MM strategy. A variance partition analysis indicated that up to 44% of the total variance was related to the choice of MM strategy. Additionally, the variance partition analysis reproduced the metabolite-age association for tCr and tCho found in the simpler correlation analysis. In summary, the inclusion of a single high signal-to-noise ratio MM basis function (cohort-mean) in the short-TE LCM leads to more lower model residuals and AIC values compared with MM strategies with more degrees of freedom (Gaussian parametrization) or subject-specific MM information. Integration of multiple LCM analyses into a single statistical model potentially allows to identify the robustness in the detection of underlying effects (e.g., metabolite vs. age), reduces algorithm-based bias, and estimates algorithm-related variance.

High-resolution 1 H-MRSI of the brain using short-TE SPICE

PURPOSE

To improve signal-to-noise ratio (SNR) for high-resolution spectroscopic imaging using a subspace-based technique known as SPectroscopic Imaging by exploiting spatiospectral CorrElation (SPICE).

METHODS

The proposed method is based on a union-of-subspaces model of MRSI signals, which exploits the partial separability properties of water, lipid, baseline and metabolite signals. Enabled by this model, a special scheme is used for accelerated data acquisition, which includes a double-echo CSI component used to collect a "training" dataset (for determination of the basis functions) and a short-TE EPSI component used to collect a sparse "imaging" dataset (for determination of the overall spatiospectral distributions). A set of signal processing algorithms are developed to remove the water and lipid signals and jointly reconstruct the metabolite and baseline signals.

RESULTS

In vivo 1 H-MRSI results show that the proposed method can effectively remove the remaining water and lipid signals from sparse MRSI data acquired at 20 ms TE. Spatiospectral distributions of metabolite signals at 2 mm in-plane resolution with good SNR were obtained in a 15.5 min scan.

CONCLUSIONS

The proposed method can effectively remove nuisance signals and reconstruct high-resolution spatiospectral functions from sparse data to make short-TE SPICE possible. The method should prove useful for high-resolution 1 H-MRSI of the brain. Magn Reson Med 77:467-479, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Compartmentalized low-rank recovery for high-resolution lipid unsuppressed MRSI

PURPOSE

To introduce a novel algorithm for the recovery of high-resolution magnetic resonance spectroscopic imaging (MRSI) data with minimal lipid leakage artifacts, from dual-density spiral acquisition.

METHODS

The reconstruction of MRSI data from dual-density spiral data is formulated as a compartmental low-rank recovery problem. The MRSI dataset is modeled as the sum of metabolite and lipid signals, each of which is support limited to the brain and extracranial regions, respectively, in addition to being orthogonal to each other. The reconstruction problem is formulated as an optimization problem, which is solved using iterative reweighted nuclear norm minimization.

RESULTS

The comparisons of the scheme against dual-resolution reconstruction algorithm on numerical phantom and in vivo datasets demonstrate the ability of the scheme to provide higher spatial resolution and lower lipid leakage artifacts. The experiments demonstrate the ability of the scheme to recover the metabolite maps, from lipid unsuppressed datasets with echo time (TE) = 55 ms.

CONCLUSION

The proposed reconstruction method and data acquisition strategy provide an efficient way to achieve high-resolution metabolite maps without lipid suppression. This algorithm would be beneficial for fast metabolic mapping and extension to multislice acquisitions. Magn Reson Med 78:1267-1280, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Semi-LASER 1 H MR spectroscopy at 7 Tesla in human brain: Metabolite quantification incorporating subject-specific macromolecule removal

PURPOSE

To develop an in vivo ¹ H short-echo-time semi-LASER spectroscopy protocol at 7 Tesla (T) incorporating subject-specific macromolecule removal.

METHODS

T₁ constants of the major metabolites were measured with little macromolecule contribution in seven healthy volunteers and used to optimize double inversion metabolite nulling. Spectra were acquired from parietal-occipital cortex of five healthy volunteers. Metabolite-nulled macromolecule spectra were subtracted from the metabolite spectra before fitting in the time domain with prior-knowledge templates. Absolute metabolite concentrations were determined by referencing to the water signal, following partial volume and relaxation corrections.

RESULTS

The average signal to noise ratio, N-acetylaspartate peak height divided by the baseline noise standard deviation, was 48 ± 6. T₁ constants for N-acetylaspartate, glutamate, creatine, and choline were 1.71 ± 0.15 s, 1.68 ± 0.19 s, 1.63 ± 0.10 s, and 1.41 ± 0.09 s, respectively. The optimal double inversion times for metabolite suppression were TI₁  = 2.09 s and TI₂  = 0.52 s. The coefficient of variation was less than 10% for N-acetylaspartate, creatine, choline, and myo-inositol, and less than 20% for glutamate and glutamine.

CONCLUSION

Short echo-time ¹ H semi-LASER spectroscopy at 7T incorporating subject-specific macromolecule removal yielded reproducible brain metabolite concentrations ideal for applications in disease conditions where macromolecule contributions may deviate from the norm. Magn Reson Med 74:4-12, 2015. © 2014 Wiley Periodicals, Inc.

Increased glutamate in the hippocampus after galantamine treatment for Alzheimer disease

Galantamine is a cholinesterase inhibitor and allosteric potentiating ligand modulating presynaptic nicotinic acetylcholine receptors that is used in the treatment of Alzheimer disease (AD). The purpose of this study was to determine if galantamine treatment would result in detectable hippocampal metabolite changes that correlated with changes in cognition, as measured by the Mini-Mental State Examination (MMSE) and the Alzheimer Disease Assessment Scale-cognitive subscale (ADAS-cog). Short echo-time proton magnetic resonance (MR) spectra were acquired from within the right hippocampus of ten patients using a 4 Tesla magnetic resonance imaging (MRI) scanner. Spectra were used to quantify absolute metabolite levels for N-acetylaspartate (NAA), glutamate (Glu), choline (Cho), creatine (Cr), and myo-inositol (mI). Patient scans and cognitive tests were performed before and 4 months after beginning galantamine treatment, which consisted of an 8 mg daily dose for the first month and a 16 mg daily dose for the remaining three months. The levels of Glu, Glu/Cr, and Glu/NAA increased after four months of treatment, while there were no changes in MMSE or ADAS-cog scores. Additionally, changes (Delta) in Glu over the four months (DeltaGlu) correlated with DeltaNAA, and Delta(Glu/Cr) correlated with DeltaMMSE scores. Increased Glu and the ratio of Glu to Cr measured by MR spectroscopy after galantamine treatment were associated with increased cognitive performance. The increase in Glu may be related to the action of galantamine as an allosteric potentiating ligand for presynaptic nicotinic acetylcholine receptors, which increases glutamatergic neurotransmission.

Challenges of using MR spectroscopy to detect neural progenitor cells in vivo

A recent report of detection of neural progenitor cells (NPCs) in living human brain by using in vivo proton MR spectroscopy ((1)H-MR spectroscopy) has sparked great excitement in the field of biomedicine because of its potential influence and utility in clinical neuroscience research. On the other hand, the method used and the findings described in the report also caused heated debate and controversy. In this article, we will briefly detail the reasons for the debate and controversy from the point of view of the in vivo (1)H-MR spectroscopy methodology and will propose some technical strategies in both data acquisition and data processing to improve the feasibility of detecting NPCs in future studies by using in vivo (1)H-MR spectroscopy.

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

In this paper an overview of time-domain and frequency-domain quantitation methods is given. Advantages and drawbacks of these two families of quantitation methods are discussed. An overview of preprocessing methods, such as lineshape correction methods or unwanted component removal methods, is also given. The choice of the quantitation method depends on the data under investigation and the pursued objectives.

High field (1)H MRS of the hippocampus after donepezil treatment in Alzheimer disease

The purpose of this study was to measure metabolite level changes in patients with newly diagnosed Alzheimer Disease (AD) following four months of donepezil treatment. A small number of cognitively normal elderly subjects were also scanned longitudinally (twice within one year) to assess the reproducibility. Short echo-time (1)H magnetic resonance spectra were acquired at 4.0 T in the right hippocampus. Subjects were scanned at the time of first diagnosis (prior to receiving donepezil) and then following four months of donepezil treatment (5 mg/day for the first month, 10 mg/day thereafter). Changes in absolute metabolite levels and metabolite ratios were quantified and compared. There was no change in measured cognitive function following four months of donepezil treatment in the AD patients. Decreased levels of N-acetylaspartate, choline, N-acetylaspartate/creatine, choline/creatine, and myo-inositol/creatine were observed in AD patients after four months of treatment. Cognitively normal elderly subjects showed an increase in myo-inositol/choline ratio following one year. The reduced levels of N-acetylaspartate in AD patients indicates continued decline in neuronal function and/or integrity. However decreased levels of choline and myo-inositol/creatine ratio may indicate a positive treatment effect.

Effect of signal-to-noise ratio and spectral linewidth on metabolite quantification at 4 T

The accuracy and precision of measurements of metabolite concentrations from short echo-time spectra has previously been characterized at l.5 T as a function of signal-to-noise ratio (SNR) and peak linewidth. The purpose of this study was to characterize the systematic error in quantification of metabolite concentrations associated with linewidth and SNR for the major metabolites of interest in the short echo-time 1H-MR spectrum at 4 T. Simulated 4 T LASER localized spectra (TE = 46 ms) were generated with full width at half maximum (FWHM) over the range 4-14 Hz, and SNR over the range 5-500 by adding 100 Gaussian-distributed noise realizations at each combination of SNR and linewidth. Linewidth and SNR were treated as independent parameters, and therefore an increase in linewidth at a constant SNR resulted in increased metabolite areas. All spectra were fitted in the time domain using identical prior-knowledge and relative parameter starting values. Six metabolites (N-acetylaspartate, glutamate, creatine, myo-inositol, glycerophosphocholine, phosphocholine) were quantified with >90% accuracy and <10% standard deviation at SNR = 10 for linewidths ranging from 8 to 14 Hz FWHM. These simulations did not consider additional sources of variation, including eddy current artifacts, incomplete macromolecule baseline removal, and incomplete water suppression. Regardless, the results show that metabolite quantification from 4 T short echo-time 1H-MRS is sensitive to SNR and linewidth.

Relaxation-selective magnetization preparation based on T1 and T2

A magnetization-preparation scheme is described that combines the spin-echo and inversion-recovery (SEIR) to select spins based on both T1 and T2 characteristics. The inclusion of T2 weighting allows for greater relative suppression of some tissues with respect to others, depending on their respective relaxation times, than does inversion-recovery alone. Formulae describing the observed magnetization following SEIR and double-SEIR (DSEIR) are presented with the corresponding formulae for inversion-recovery (IR) and double-IR (DIR). The formulae are validated with experimental studies on MnCl2 solutions and compared numerically for a variety of possible applications. Results indicate that DSEIR may yield 2x or more signal than DIR in some potential applications.

High-field localized 1H NMR spectroscopy in the anesthetized and in the awake monkey

Localized cerebral in vivo 1H NMR spectroscopy (MRS) was performed in the anesthetized as well as the awake monkey using a novel vertical 7 T/60 cm MR system. The increased sensitivity and spectral dispersion gained at high field enabled the quantification of up to 16 metabolites in 0.1- to 1-ml volumes. Quantification was accomplished by using simulations of 18 metabolite spectra and a macromolecule (MM) background spectrum consisting of 12 components. Major cerebral metabolites (concentrations >3 mM) such as glutamate (Glu), N-acetylaspartate (NAA), creatine (Cr)/phosphocreatine (PCr) and myo-inositol (Ins) were identified with an error below 3%; most other metabolites were quantified with errors in the order of 10%. Metabolite ratios were 1.39:1 for total NAA, 1.38:1 for glutamate (Glu)/glutamine (Gln) and 0.09:1 for cholines (Cho) relative to total Cr. Taurine (Tau) was detectable at concentrations lower than 1 mM, while lactate (Lac) remained below the detection limit. The spectral dispersion was sufficient to separate metabolites of similar spectral patterns, such as Gln and Glu, N-acetylaspartylglutamate (NAAG) and NAA, and PCr-Cr. MRS in the awake monkey required the development and refinement of acquisition and correction strategies to minimize magnetic susceptibility artifacts induced by respiration and movement of the mouth or body. Periods with major motion artifacts were rejected, while a frequency/phase correction was performed on the remaining single spectra before averaging. In resting periods, both spectral amplitude and line width, that is, the voxel shim, were unaffected permitting reliable measurements. The corrected spectra obtained from the awake monkey afforded the reliable detection of 6-10 cerebral metabolites of 1-ml volumes.

Molality as a unit of measure for expressing 1H MRS brain metabolite concentrations in vivo

Absolute concentrations of cerebral metabolite in in vivo 1H magnetic resonance spectroscopy studies (1H-MRS) are widely reported in molar units as moles per liter of tissue, or in molal units as moles per kilogram of tissue. Such measurements require external referencing or assumptions as to local water content. To reduce the scan time, avoid assumptions that may be invalid under specific pathologies, and provide a universally accessible referencing procedure, we suggest that metabolite concentrations from 1H-MRS measurements in vivo be reported in molal units as moles per kilogram of tissue water. Using internal water referencing, a two-compartment water model, a simulated brain spectrum for peak identification, and a spectroscopic bi-exponential spin-spin relaxation segmentation technique, we measured the absolute concentrations for the four common 1H brain metabolites: choline (Cho), myo-inositol (mIno), phosphocreatine + creatine (Cr), and N-acetyl-aspartate (NAA), in the hippocampal region (n = 26) and along the Sylvian fissure (n = 61) of 35 healthy adults. A stimulated echo localization method (20 ms echo time, 10 ms mixing time, 4 s repetition time) yielded metabolite concentrations, uncorrected for metabolite relaxation or contributions from macromolecule resonances, that were expectantly higher than with molar literature values. Along the Sylvian fissure the average concentrations (coefficient of variation (CV)) in mmoles/kg of tissue water were 17.6 (12%) for NAA, 14.2 (9%) for Cr, 3.6 (13%) for Cho, and 13.2 (15%) for mIno. Respective values for the hippocampal region were 15.7 (20%), 14.7 (16%), 4.6 (19%), and 17.7 (26%). The concentrations of the two regions were significantly different (p </= 0.001) for NAA, mIno, and Cho, a trend in agreement with previous studies. All gray matter Sylvian fissure CV values, except for NAA, were also in agreement with previous 1H-MRS gray matter studies. The reduced precision of the NAA concentration was attributed to overlapping signal contributions from glutamate and glutamine (Glx), suggesting that a detailed Glx model is critical for accurate quantitation of the NAA 2.02 ppm resonance. The reduced precision of the measurements in the hippocampal region was attributed to poor spectral resolution.

Quantitative proton short-echo-time LASER spectroscopy of normal human white matter and hippocampus at 4 Tesla incorporating macromolecule subtraction

Accurate quantification of in vivo short-echo-time (TE) (1)H spectra must account for contributions from both mobile metabolites and less mobile macromolecules, which can fluctuate in disease. The purpose of this study was to develop an approach for the acquisition and processing of macromolecule information to optimize metabolite quantification accuracy and precision. Human parietal white matter (8-cm(3) voxel) and posterior hippocampus (1.7-cm(3) voxel) metabolite levels were quantified, following manomolecule subtraction, from short-echo-time spectra (TE = 46 ms) acquired at 4.0 Tesla with localization by adiabatic selective refocusing (LASER). Nineteen metabolites were fit using a time domain Levenberg-Marquardt minimization that incorporated prior knowledge of metabolite lineshapes. The macromolecule contribution to the spectrum was reduced by 87% (P < 0.05) when the acquisition of single averages of the full spectrum and macromolecule spectrum were interleaved to reduce subtraction errors due to motion. Subtracting the Hankel Lanczos singular value decomposition (HLSVD) fit of the macromolecule spectrum, which contained no random noise, did not alter quantified metabolite levels but did not increase metabolite quantification precision. Several metabolites had higher concentrations in the posterior hippocampus compared to parietal white matter, which emphasizes the need to carefully control for partial volume contamination in hippocampal spectroscopy studies.

Comparison of inversion recovery preparation schemes for lipid suppression in 1H MRSI of human brain

To reduce contamination from subcutaneous lipid regions in MR spectroscopic imaging (MRSI) of whole brain, lipid signals are often suppressed using T(1) nulling methods. If a range of lipid T(1) values is present, the suppression efficiency will be improved using multiple inversion recovery (MIR) preparation. This study compared single IR (SIR) and double IR (DIR) applied with a volumetric MRSI sequence at 1.5 T based on experimental measurement of lipid T(1) and T(2) relaxation rates. At short and medium echo times (TEs), an approximately 28-47% improvement in lipid suppression was achieved with DIR compared to SIR. However, it also led to a loss of 37-43% in signal-to-noise ratio (SNR) for metabolites. Thus, SIR appears to be the better choice for suppressing lipid signals and maintaining metabolite sensitivity.

The depletion of protein signals in metabonomics analysis with the WET-CPMG pulse sequence

Nuclear magnetic resonance (NMR) spectroscopy is a powerful analytical tool capable of providing a comprehensive metabolic profile of biofluids such as urine, plasma, and serum. Unfortunately, when measuring serum and plasma, the high protein concentration can obscure the signals originating from low molecular weight metabolites. We evaluated the use of different parameters within the Carr-Purcell-Meiboom-Gill (CPMG) pulse train of fast spin-echoes to remove the macromolecular signal contribution in one-dimensional proton (1H) NMR spectra. Experimental parameters such as the refocusing delay in the CPMG pulse train, pulse miscalibration, and recycle time were examined to assess the ability to remove the protein signals from the spectrum without causing a deleterious effect on the signals originating from free, low molecular weight metabolites. The 1H-NMR spectra of a variety of serum samples spiked with 2'-deoxyadenosine were acquired using various acquisition parameters. Our results show that the delay used in the CPMG spin-echo and the combination of the acquisition pulse flip angle and recycle time are the two major factors affecting the observed metabolite signal amplitudes in the resulting 1H-NMR spectrum.

Constrained modeling for spectroscopic measurement of bi-exponential spin-lattice relaxation of water in vivo

The (1)H NMR water signal from spectroscopic voxels localized in gray matter contains contributions from tissue and cerebral spinal fluid (CSF). A typically weak CSF signal at short echo times makes separating the tissue and CSF spin-lattice relaxation times (T(1)) difficult, often yielding poor precision in a bi-exponential relaxation model. Simulations show that reducing the variables in the T(1) model by using known signal intensity values significantly improves the precision of the T(1) measurement. The method was validated on studies on eight healthy subjects (four males and four females, mean age 21 +/- 2 years) through a total of twenty-four spectroscopic relaxation studies. Each study included both T(1) and spin-spin relaxation (T(2)) experiments. All volumes were localized along the Sylvian fissure using a stimulated echo localization technique with a mixing time of 10 ms. The T(2) experiment consisted of 16 stimulated echo acquisitions ranging from a minimum echo time (TE) of 20 ms to a maximum of 1000 ms, with a repetition time of 12 s. All T(1) experiments consisted of 16 stimulated echo acquisition, using a homospoil saturation recovery technique with a minimum recovery time of 50 ms and a maximum 12 s. The results of the T(2) measurements provided the signal intensity values used in the bi-exponential T(1) model. The mean T(1) values when the signal intensities were constrained by the T(2) results were 1055.4 ms +/- 7.4% for tissue and 5393.5 ms +/- 59% for CSF. When the signal intensities remained free variables in the model, the mean T(1) values were 1085 ms +/- 19.4% and 5038.8 ms +/- 113.0% for tissue and CSF, respectively. The resulting improvement in precision allows the water tissue T(1) value to be included in the spectroscopic characterization of brain tissue.

Assessment of 3D proton MR echo-planar spectroscopic imaging using automated spectral analysis

For many clinical applications of proton MR spectroscopic imaging (MRSI) of the brain, diagnostic assessment is limited by insufficient coverage provided by single- or multislice acquisition methods as well as by the use of volume preselection methods. Additionally, traditional spectral analysis methods may limit the operator to detailed analysis of only a few selected brain regions. It is therefore highly desirable to use a fully 3D approach, combined with spectral analysis procedures that enable automated assessment of 3D metabolite distributions over the whole brain. In this study, a 3D echo-planar MRSI technique has been implemented without volume preselection to provide sufficient spatial resolution with maximum coverage of the brain. Using MRSI acquisitions in normal subjects at 1.5T and a fully automated spectral analysis procedure, an assessment of the resultant spectral quality and the extent of viable data in human brain was carried out. The analysis found that 69% of brain voxels were obtained with acceptable spectral quality at TE = 135 ms, and 52% at TE = 25 ms. Most of the rejected voxels were located near the sinuses or temporal bones and demonstrated poor B0 homogeneity and additional regions were affected by stronger lipid contamination at TE = 25 ms.

Characterization of the macromolecule baseline in localized (1)H-MR spectra of human brain

Short-echo-time magnetic resonance spectra of human brain contain broad contributions from macromolecules. As they are a priori of unknown shape and intensity, they pose a problem if one wants to quantitate the overlying spectral features from low-molecular-weight metabolites. On the other hand, the macromolecular contributions may provide relevant clinical information themselves, if properly evaluated. Several methods, based on T(1), T(2), or spectral shape, have previously been suggested to suppress or edit the macromolecule contributions. Here, a method is presented based on a series of saturation recovery scans and that allows for simultaneous recording of the macromolecular baseline and the fully relaxed metabolite spectrum. In comparison to an inversion recovery technique aimed at nulling signals from long-T(1) components, the saturation recovery method is less susceptible to T(1) differences inherent in signals from different metabolites or introduced by pathology. The saturation recovery method was used to quantitate the macromolecular baseline in white and/or gray matter locations of the human brain in 40 subjects. It was found that the content and composition of MR visible macromolecules depends on cerebral location, as well as the age of the investigated subject, while no gender dependence could be found.

Reliable detection of macromolecules in single-volume 1H NMR spectra of the human brain

In short echo time proton MR spectra of the brain, resonances from macromolecules are visible. The macromolecular resonances in the 0.5-2.0 ppm region can be affected by lipid contamination arising from fat-containing regions outside the selected volume of interest (VOI). This study demonstrates that considerable lipid contamination may remain in stimulated echo acquisition mode (STEAM) spectra even if the spoiling of unwanted coherences is sufficient and the VOI is placed 2 cm or more away from fat-containing regions. The observed contamination was attributed to residual remote out-of-volume excitation, although only very small out-of-slice ripples of less than 0.2% of the in-slice excitation were found in the calculated excitation profile of the RF pulses. Spatial presaturation of fat-containing regions led to a sufficient suppression of the contamination and enabled the detection of highly reproducible macromolecular resonances. Thus, in single-volume spectroscopy as well as in spectroscopic imaging (SI or CSI), the combination of volume selection and outer volume presaturation, each in three dimensions, is highly recommended to ensure accurate detection and reliable evaluation of even small pathological alterations in macromolecules, e.g., proteins or lipids, or other resonances in the 0.5-2.0 ppm region.

Current awareness

In order to keep subscribers up-to-date with the latest developments in their field, John Wiley & Sons are providing a current awareness service in each issue of the journal. The bibliography contains newly published material in the field of NMR in biomedicine. Each bibliography is divided into 9 sections: 1 Books, Reviews ' Symposia; 2 General; 3 Technology; 4 Brain and Nerves; 5 Neuropathology; 6 Cancer; 7 Cardiac, Vascular and Respiratory Systems; 8 Liver, Kidney and Other Organs; 9 Muscle and Orthopaedic. Within each section, articles are listed in alphabetical order with respect to author. If, in the preceding period, no publications are located relevant to any one of these headings, that section will be omitted.