Whole-Brain N-Acetylaspartate Concentration Is Preserved during Mild Hypercapnia Challenge.

BACKGROUND AND PURPOSE: Although NAA is often used as a marker of neuronal health and integrity in neurologic disorders, its normal response to physiologic challenge is not well-established and its changes are almost always attributed exclusively to brain pathology. The purpose of this study was to test the hypothesis that the neuronal cell marker NAA, often used to assess neuronal health and integrity in neurologic disorders, is not confounded by (possibly transient) physiologic changes. Therefore, its decline, when observed by using (1)H-MR spectroscopy, can almost always be attributed exclusively to brain pathology. MATERIALS AND METHODS: Twelve healthy young male adults underwent a transient hypercapnia challenge (breathing 5% CO2 air mixture), a potent vasodilator known to cause a substantial increase in CBF and venous oxygenation. We evaluated their whole-brain NAA by using nonlocalizing proton MR spectroscopy, venous oxygenation with T2-relaxation under spin-tagging MR imaging, CBF with pseudocontinuous arterial spin-labeling, and the cerebral metabolic rate of oxygen, during normocapnia (breathing room air) and hypercapnia. RESULTS: There was insignificant whole-brain NAA change (P = .88) from normocapnia to hypercapnia and back to normocapnia in this cohort, as opposed to highly significant increases: 28.0 ± 10.3% in venous oxygenation and 49.7 ± 16.6% in global CBF (P < 10(-4)); and a 6.4 ± 10.9% decrease in the global cerebral metabolic rate of oxygen (P = .04). CONCLUSIONS: Stable whole-brain NAA during normocapnia and hypercapnia, despite significant global CBF and cerebral metabolic rate of oxygen changes, supports the hypothesis that global NAA changes are insensitive to transient physiology. Therefore, when observed, they most likely reflect underlying pathology resulting from neuronal cell integrity/viability changes, instead of a response to physiologic changes.

Comprehensive processing, display and analysis for in vivo MR spectroscopic imaging.

Image reconstruction for magnetic resonance spectroscopic imaging (MRSI) requires specialized spatial and spectral data processing methods and benefits from the use of several sources of prior information that are not commonly available, including MRI-derived tissue segmentation, morphological analysis and spectral characteristics of the observed metabolites. In addition, incorporating information obtained from MRI data can enhance the display of low-resolution metabolite images and multiparametric and regional statistical analysis methods can improve detection of altered metabolite distributions. As a result, full MRSI processing and analysis can involve multiple processing steps and several different data types. In this paper, a processing environment is described that integrates and automates these data processing and analysis functions for imaging of proton metabolite distributions in the normal human brain. The capabilities include normalization of metabolite signal intensities and transformation into a common spatial reference frame, thereby allowing the formation of a database of MR-measured human metabolite values as a function of acquisition, spatial and subject parameters. This development is carried out under the MIDAS project (Metabolite Imaging and Data Analysis System), which provides an integrated set of MRI and MRSI processing functions. It is anticipated that further development and distribution of these capabilities will facilitate more widespread use of MRSI for diagnostic imaging, encourage the development of standardized MRSI acquisition, processing and analysis methods and enable improved mapping of metabolite distributions in the human brain.

Robust analysis of short echo time (1)H MRSI of human brain.

Short echo time proton MR Spectroscopic Imaging (MRSI) suffers from low signal-to-noise ratio (SNR), limiting accuracy to estimate metabolite intensities. A method to coherently sum spectra in a region of interest of the human brain by appropriate peak alignment was developed to yield a mean spectrum with increased SNR. Furthermore, principal component (PC) spectra were calculated to estimate the variance of the mean spectrum. The mean or alternatively the first PC (PC(1)) spectrum from the same region can be used for quantitation of peak areas of metabolites in the human brain at increased SNR. Monte Carlo simulations showed that both mean and PC(1) spectra were more accurate in estimating regional metabolite concentrations than solutions that regress individual spectra against the tissue compositions of MRSI voxels. Back-to-back MRSI studies on 10 healthy volunteers showed that mean spectra markedly improved reliability of brain metabolite measurements, most notably for myo-inositol, as compared to regression methods.

A fully automated method for tissue segmentation and CSF-correction of proton MRSI metabolites corroborates abnormal hippocampal NAA in schizophrenia.

In this report, we describe the implementation and application of a fully automated segmentation routine using SPM99 algorithms and MATLAB for clinical Magnetic Resonance Spectroscopic Imaging (MRSI) studies. By segmenting high-resolution 3-D image data and coregistering the results to the spatial localizer slices of a spectroscopy examination, the program offers the possibility to easily calculate segmentation maps for a large variety of MRSI experiments. The segmented data are corrected for the individual point-spread function, slice and VOI profiles for measurement sequences with selective pulses as well as for the chemical shifts of different metabolites. The new method was applied to investigate discrete hippocampal metabolite abnormalities in a small sample of schizophrenic patients in comparison to healthy controls (15 patients, 15 controls). Only after correction was the N-acetyl-aspartate (NAA) signal significantly lower in patients compared to controls. No differences were found for the corrected signals from the creatine/phosphocreatine (Cr) or choline-containing compounds (Ch). These results are in good agreement with neuropathological and previous MR spectroscopy studies of the hippocampus in schizophrenic patients.

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.

Short echo time multislice proton magnetic resonance spectroscopic imaging in human brain: metabolite distributions and reliability.

Multislice proton magnetic resonance spectroscopic imaging (1H MRSI) at 25 ms echo time was used to measure concentrations of myo-inositol (mI), N-acetylaspartate (NAA), and creatine (Cr) and choline (Cho) in ten normal subjects between 22 and 84 years of age (mean age 44 +/- 18 years). By co-analysis with MRI based tissue segmentation results, metabolite distributions were analyzed for each tissue type and for different brain regions. Measurement reliability was evaluated using intraclass correlation coefficients (ICC). Significant differences in metabolite distributions were found for all metabolites. mI of frontal gray matter was 84% of parietal gray matter and 87% of white matter. NAA of frontal gray matter was 86% of parietal gray matter and 85% of white matter. Cho of frontal gray matter was 125% of parietal gray matter and 59% of white matter and Cho of parietal gray matter was 47% of white matter. Cr of parietal gray matter was 113% of white matter. Reliability was relatively high (ICC from.70 to.93) for all metabolites in white matter and for NAA and Cr in gray matter, though limited (ICC less than.63) for mI and Cho in gray matter. These findings indicate that voxel gray/white matter contributions, regional variations in metabolite concentrations, and reliability limitations must be considered when interpreting 1H MR spectra of the brain.

Representation of strong baseline contributions in 1H MR spectra.

A comparison is made between two optimization procedures and two data models for automated analysis of in vivo proton MR spectra of brain, typical of that obtained using MR spectroscopic imaging at 1.5 Tesla. First, a shift invariant wavelet filter is presented that provides improved performance over a conventional wavelet filter method for characterizing smoothly varying baseline signals. Next, two spectral fitting methods are described: an iterative spectral analysis method that alternates between optimizing a parametric description of metabolite signals and nonparametric characterization of baseline contributions, and a single-pass method that optimizes a complete spectral and baseline model. Both methods are evaluated using wavelet and spline models of the baseline function. Results are shown for Monte Carlo simulations of data representative of both long and short TE, in vivo 1H acquisitions.

Short TE in vivo (1)H MR spectroscopic imaging at 1.5 T: acquisition and automated spectral analysis.

Spectral analysis of short TE in vivo proton magnetic resonance spectroscopic imaging (MRSI) data are complicated by the presence of spectral overlap, low signal to noise and uncharacterized signal contributions. In this study, it is shown that an automated data analysis method can be used to generate metabolite images from MRSI data obtained from human brain at TE = 25 ms and 1.5 T when optimized pulse sequences and a priori metabolite knowledge are used. The analysis approach made use of computer simulation methods to obtain a priori spectral information of the metabolites of interest and utilized a combination of parametric spectral modeling and non-parametric signal characterization for baseline fitting. This approach was applied to data from optimized PRESS-SI and multi-slice spin-echo SI acquisitions, for which sample spectra and metabolite images are shown.

Confidence images for MR spectroscopic imaging.

Automated spectral analysis and estimation of signal amplitudes from magnetic resonance data generally constitutes a difficult nonlinear optimization problem. Obtaining a measure of the degree of confidence that one has in the estimated parameters is as important as the estimates themselves. This is particularly important if clinical diagnoses are to be based on estimated metabolite levels, as in applications of MR Spectroscopic Imaging for human studies. In this report, a standard method of obtaining confidence intervals for nonlinear estimation is applied to simulated data and short-TE clinical proton spectroscopic imaging data sets of human brain. So-called "confidence images" are generated to serve as visual indicators of how much trust should be placed in interpretation of spatial variations seen in images derived from fitted metabolite parameter estimates. This method is introduced in a Bayesian framework to enable comparison with similar techniques using Cramer-Rao bounds and the residuals of fitted results.

1H MRSI comparison of white matter and lesions in primary progressive and relapsing-remitting MS.

OBJECTIVE: To compare brain metabolite levels in patients with primary progressive (PP) and relapsing remitting (RR) MS and controls. HYPOTHESES: (1) creatine (Cr), a putative marker of gliosis, is elevated and N-acetylaspartate (NAA), a putative marker of axonal density and functional integrity, is reduced in PPMS lesions and normal appearing white matter (NAWM) compared to control white matter; (2) The pattern of metabolite change in PPMS is different than in RRMS. METHODS: MRI and proton magnetic resonance spectroscopic imaging (1H MRSI) were collected from 15 PPMS patients, 13 RRMS patients, and 20 controls. RESULTS: Cr was increased in PPMS NAWM compared to controls (P=0.035), and compared to RRMS NAWM (P=0.038). Cr was increased in focal MRI lesions from PPMS compared to lesions from RRMS (P=0.044) and compared to control white matter (P=0.041). NAA was similarly reduced in PPMS and RRMS NAWM compared to control. NAA was similarly reduced in PPMS and RRMS lesions, compared to control white matter. CONCLUSIONS: Creatine is higher in PPMS than RRMS NAWM and focal lesions. This observation is consistent with the notion that progressive disability in PPMS reflects increased gliosis and axonal loss whereas disability in RRMS reflects the cumulative effects of acute inflammatory lesions and axonal loss.

Automated spectral analysis I: formation of a priori information by spectral simulation.

A spectral simulation method is described for generating a priori information for use in parametric spectral analysis. The method makes use of GAMMA (S. A. Smith, T. O. Levante, B. H. Meier, R. R. Ernst, J. Magn. Reson., 106A, 75-105, 1994), a programming environment that facilitates simulation of magnetic resonance phenomena. The input parameters consist of the chemical shifts and scalar spin-coupling constants for the compounds to be analyzed, the acquisition pulse sequence, and the field strength used. The resultant spectral information consists of the relative amplitude, frequency, and phase of all resonances, which are stored in a spectral database. This procedure can be rapidly and conveniently modified to reflect different acquisition parameters and data analysis requirements.

Automated spectral analysis II: application of wavelet shrinkage for characterization of non-parameterized signals.

An iterative method for differentiating between known resonances and uncharacterized baseline contributions in MR spectra is described. The method alternates parametric modeling, using a priori knowledge of spectral parameters, with non-parametric characterization of remaining signal components, using wavelet shrinkage and denoising. Rapid convergence of the iterative method is demonstrated, and examples are shown for analysis of simulated data and an in vivo 1H spectrum from the brain. Results show good separation between metabolite signals and strong baseline contributions.

Automated spectral analysis III: application to in vivo proton MR spectroscopy and spectroscopic imaging.

An automated method for analysis of in vivo proton magnetic resonance (MR) spectra and reconstruction of metabolite distributions from MR spectroscopic imaging (MRSI) data is described. A parametric spectral model using acquisition specific, a priori information is combined with a wavelet-based, nonparametric characterization of baseline signals. For image reconstruction, the initial fit estimates were additionally modified according to a priori spatial constraints. The automated fitting procedure was applied to four different examples of MRS data obtained at 1.5 T and 4.1 T. For analysis of major metabolites at medium TE values, the method was shown to perform reliably even in the presence of large baseline signals and relatively poor signal-to-noise ratios typical of in vivo proton MRSI. Identification of additional metabolites was also demonstrated for short TE data. Automated formation of metabolite images will greatly facilitate and expand the clinical applications of MR spectroscopic imaging.

Magnetic resonance perfusion imaging in acute middle cerebral artery stroke: comparison of blood volume and bolus peak arrival time.

Ten patients with a diagnosis of acute middle cerebral artery stroke were evaluated using perfusion magnetic resonance imaging (MRI) during bolus injection of gadolinium diethylenetriaminepentaacetic acid (GdDTPA), MR angiography, and conventional MRI. Scans were performed within 24 hours of symptoms, onset, and 5 of the 10 patients had follow-up MR scans 3 or more days later to determine radiological outcome. Perfusion data were analyzed in terms of relative regional cerebral blood volume (rCBV) and bolus peak arrival times (BAT). Although relative rCBV values overall showed no significant changes compared with contralateral regions of interest, BAT was significantly increased in both infarct and peri-infarct regions. Areas of abnormal BAT significantly exceeded areas of T(2) hyperintensity in acute studies; follow-up images indicated that the size of infarction increased to include some regions with previously abnormal BAT. BAT appears to be a more sensitive parameter for the detection of abnormal cerebral perfusion than rCBV. Used in conjunction with other MR methods, perfusion MR imaging may allow visualization of ischemic tissue at risk of infarction in acute stroke.

Object shape processing in the visual system evaluated using functional MRI.

We used functional MRI (fMRI) to determine the cortical regions activated during processing of visual object shape in humans in six men and three women, using a paradigm with a baseline condition of simple shape detection and an activated condition of object/nonobject shape discrimination. Eight of the nine subjects studied showed significant signal changes. Seven of eight showed changes in the occipital lobes (five bilateral, two right only, one left only). All eight subjects with signal changes exhibited changes in the parietal lobes bilaterally. In the occipitotemporal gyri, there were signal changes bilaterally in seven subjects and unilaterally, on the right, in one. Activation-related fMRI signal increases were also present in the posterior superior and middle temporal gyri in seven of the subjects, with four showing bilateral signal changes, two showing signal changes on the left only, and one only on the right. The data strongly suggest that processing of object shape information in humans activates both the ventral and dorsal visual processing pathways ("what" and "where" pathways), described previously both in humans and in nonhuman primates.

Quantitation of automated single-voxel proton MRS using cerebral water as an internal reference.

Data from a previously published, multi-site trial (P.G. Webb, N. Sailasuta, S.J. Kohler, T. Raidy, R.A. Moats, R.E. Hurd. Automated single-voxel proton MRS: technical development and multisite verification. Magn. Reson. Med. 31, 365-373 (1994)) of a fully automatic, single-voxel, proton spectroscopy package (PROBE/SV, GE Medical Systems) was re-analyzed in terms of absolute metabolite concentrations using the cerebral water signal as an internal reference. In 100 spectra from parietal white matter in normal volunteers ranging in age from 22 to 34 years at eight sites, overall concentrations of choline (Cho) creatine (Cr), and N-acetyl-aspartate (NAA) resonances were found to be 2.00 +/- 0.50, 8.43 +/- 1.28, and 12.55 +/- 1.76 mumol/g wet weight, respectively. These values are in good general agreement with previously published values from quantitative, single-voxel studies. Metabolite concentrations for NAA, Cr, and Cho across all sites had standard deviations of 14.1%, 14.9%, and 25.1%, respectively. Quantitation of PROBE data sets is routinely possible by using the cerebral water signal as an internal reference.

Quantitative proton MR spectroscopic imaging of the human brain.

Multislice proton MR spectroscopic images (SI) of the brain were quantitated, using the phantom replacement technique. In 16 normal volunteers, ranging in age from 5 to 74 years, average "whole brain" concentrations of choline (Cho), creatine (Cr), and N-acetylaspartate (NAA) were found to be 2.4 +/- 0.4, 7.9 +/- 1.3, and 11.8 +/- 1.0 (mM, mean +/- SD), respectively. These values are in good general agreement with those previously determined by single-voxel localization techniques. Cortical gray matter was found to have lower Cho and NAA levels, compared to those of white matter, corpus callosum, and basal ganglia. Cho was also found to increase significantly with age in several locations. Quantitative multislice proton Si is feasible in the clinical environment, and regional and age-dependent variations occur that must be accounted for when evaluating spectra from pathological conditions.

Comparison of functional MR and H2 15O positron emission tomography in stimulation of the primary visual cortex.

PURPOSE: To locate spoiled gradient-echo functional MR signal changes in relation to brain parenchyma. METHODS: The region of the primary visual cortex was evaluated using functional MR and H2 15O positron emission tomography in each of six male subjects who were being visually stimulated by means of red light-emitting diode flash goggles. RESULTS: The positron emission tomography technique demonstrated substantially greater relative signal change with visual stimulation than did the functional MR technique. Furthermore, the functional MR signal changes were concentrated in loci around the periphery of brain parenchyma exhibiting increased radiotracer activity, as opposed to being collocated. CONCLUSIONS: Signal changes found using functional MR based on gradient-echo techniques reflect primarily phenomena occurring within small veins and underrepresent activity intrinsic to brain parenchyma, thus introducing potential inaccuracies in locating regions of activated brain tissue. Positron emission tomography, however, directly measures changes in metabolically related activity within the parenchyma.

Proton magnetic resonance spectroscopy and gadolinium-DTPA perfusion imaging of asymptomatic MRI white matter lesions.

In the elderly, asymptomatic white matter hyperintensities are common on T2-weighted magnetic resonance imaging (MRI). In symptomatic patients, such MRI appearances correlate with varied postmortem findings including demyelination or stroke. What structural correlates underlie the T2 hyperintensities in patients whose lesions are asymptomatic is controversial. Therefore, in order to investigate the underlying metabolism and perfusion in white matter lesions (exhibiting T2 hyperintensity), 13 patients underwent proton magnetic resonance spectroscopy and dynamic gadolinium-DTPA perfusion-weighted MR imaging. N-acetyl aspartate (NA) levels were reduced in the lesions compared with age-matched controls (P = 0.031), implying neuronal/axonal loss. Creatine levels were also reduced (P = 0.001). Choline levels were unchanged in the lesions. Lactate was identified in the lesions of 5 of the 13 patients. Although not statistically significant, perfusion studies exhibited a trend toward lower cerebral blood volumes in patients with high grade extracranial carotid stenosis and lactate-containing lesions. These findings suggest that neuronal/axonal loss underlies the majority of T2-weighted asymptomatic lesions in the older population, and in many cases these changes may be due to chronic ischemia.

Acute stroke: evaluation with serial proton MR spectroscopic imaging.

PURPOSE: To investigate the evolution of metabolic changes detectable with proton magnetic resonance (MR) spectroscopic imaging in acute stroke and to compare these findings with those of conventional MR imaging. MATERIALS AND METHODS: A patient with middle cerebral artery stroke underwent conventional proton-density (PD)- and T2-weighted MR imaging, MR angiography, and multisection proton two-dimensional MR spectroscopic imaging over a period of 3 hours to 5 months after symptom onset. RESULTS: On 3-hour MR images, no abnormal signal intensity change was detectable. Spectroscopic images obtained at 24 hours showed localized elevation of cerebral lactate levels. In most regions with high lactate levels, infarction subsequently occurred. In the chronic stage (5 months), the infarct was associated with reduced N-acetylaspartate levels, increased choline levels, and absence of lactate. CONCLUSION: Spectroscopic imaging enables mapping of ischemic and infarcted brain regions with greater sensitivity than does conventional MR imaging.