Background-signal Parameterization in In Vivo MR Spectroscopy (original) (raw)
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Quantification of (1) H-MRS signals based on sparse metabolite profiles in the time-frequency domain
NMR in biomedicine, 2017
MRS is an analytical approach used for both quantitative and qualitative analysis of human body metabolites. The accurate and robust quantification capability of proton MRS ((1) H-MRS) enables the accurate estimation of living tissue metabolite concentrations. However, such methods can be efficiently employed for quantification of metabolite concentrations only if the overlapping nature of metabolites, existing static field inhomogeneity and low signal-to-noise ratio (SNR) are taken into consideration. Representation of (1) H-MRS signals in the time-frequency domain enables us to handle the baseline and noise better. This is possible because the MRS signal of each metabolite is sparsely represented, with only a few peaks, in the frequency domain, but still along with specific time-domain features such as distinct decay constant associated with T2 relaxation rate. The baseline, however, has a smooth behavior in the frequency domain. In this study, we proposed a quantification method ...
Magnetic …, 2011
TARQUIN, a new method for the fully automatic analysis of short echotime in-vivo 1 H MRS 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 employed to improve fitting stability. Initial point truncation and HSVD water removal are used to reduce baseline interference. Three methods were used to test performance. Firstly, metabolite concentrations from six healthy volunteers at 3T were compared with LCModel TM . Secondly, a Monte-Carlo simulation was performed and results were compared with LCModel TM to test the accuracy of the new method.
Spectral decomposition for resolving partial volume effects in MRSI
Magnetic Resonance in Medicine, 2017
Purpose-Estimation of brain metabolite concentrations by MR spectroscopic imaging (MRSI) is complicated by partial volume contributions from different tissues. This study evaluates a method for increasing tissue specificity that incorporates prior knowledge of tissue distributions. Methods-A spectral decomposition technique was evaluated for separation of spectra from white-matter and gray-matter and for measurements in small brain regions using whole-brain MRSI. Simulation and in vivo studies compare results of metabolite quantifications obtained using the spectral decomposition technique to those obtained by spectral fitting of individual voxels, using mean values and linear regression against tissue fractions, and spectral fitting of regionally integrated spectra. Results-Simulation studies showed that for gray-matter and the putamen, the spectral decomposition method offers <2% and 3.5% error, respectively, in metabolite estimates. These errors are considerably reduced in comparison to methods that do not account for partial volume effects or use regressions against tissue fractions. In an analysis of data from 197 studies, significant differences in mean metabolite values, and changes with age were found. Spectral decomposition resulted in significantly better linewidth, SNR and spectral fitting quality as compared to individual spectral analysis. Moreover, significant partial volume effects were seen on correlations of neurometabolite estimates with age. Conclusion-The spectral decomposition analysis approach is of considerable value in studies of pathologies that may preferentially affect white or gray-matter, and smaller brain regions significantly affected by partial volume effects.
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Journal of Magnetic Resonance, 2007
An application that provides a flexible and easy to use interface to the GAMMA spectral simulation package is described that is targeted at investigations using in vivo MR spectroscopic methods. The program makes available a number of widely used spatially-localized MRS pulse sequences and NMR parameters for commonly-observed tissue metabolites, enabling spectra to be simulated for any pulse sequence parameter and viewed in an integrated display. The application is interfaced with a database for storage of all simulation parameters and results of the simulations. This application provides a convenient method for generating a priori spectral information used in parametric spectral analyses and for visual examination of the effects of difference pulse sequences and parameter settings.
Short TE in vivo 1H MR spectroscopic imaging at 1.5 T: acquisition and automated spectral analysis
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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.
Signal disentanglement in in vivo mr spectroscopy: By semi-parametric processing or by measurement?
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In practice, a signal often comprises a parametric part and a non-parametric part. Disentangling the two parts from each other is a universal problem. This study concerns two methods of disentangling in the field of in vivo Magnetic Resonance Spectroscopy (MRS) where metabolites contribute the parametric part and so-called macromolecules the non-parametric part. One method is based on semi-parametric estimation using a priori knowledge and was treated at length by us in Proc. ProRISC 2005. This year, automation of the semi-parametric method is treated. The other method is based on separate, additional measurement of the macromolecules-only contribution, at the cost of doubling the total measurement time. Ideally, the macromolecule contribution can then be simply subtracted, thus yielding the metabolite-only contribution. We analyse advantages and disadvantages of the two methods by way of Monte-Carlo simulations and Cramér-Rao theory.
The Filtering Approach to Solvent Peak Suppression in MRS: A Critical Review
Journal of Magnetic Resonance, 2001
Suppressing the solvent peak is important in many applications of biomedical NMR spectroscopy in order to quantify the metabolites with a great accuracy. Among the postprocessing methods proposed in the literature, many deal with the concept of filtering. However, several proposals lack a theoretical perspective and some have not been explicitly applied to quantification problems. The present article is intended to bridge this gap: five methods are analyzed from a theoretical perspective. Subsequently the different methods are applied to the same set of data, and then the latter are quantified using the model fitting method AMARES. With our set, the scheme proposed by T. Sundin et al. (J. Magn. Reson. 139(2), 189-204 (1999)) proved to be the most reliable method.
In vivo1H MR spectra analysis by means of second derivative method
Magma: Magnetic Resonance Materials in Physics, Biology, and Medicine, 2001
Short echo time (TE) in vivo PRESS ~H MR spectra (2 T, TE = 35 ms) of normal brain were fitted in the frequency domain using the second derivative method. In this approach, local maxima and hidden peaks are found as local minima of spectrum second derivative. The Lorentzian robust minirnisation procedure (referred to as maximum likelihood or m-estirnate fitting) using Levenburg-Marquardt non-linear fitting engine was applied. Spectral lines were approximated under the assumption of the mixed Lorentzian/Gaussian lineshapes. The same procedure was applied to 18 proton spectra. The number of peaks found within the range of 0.74/4.2 parts per million (ppm) was 52 _+ 3 and their positions were almost the same. The fitted lines were assigned on the basis of the J-pattern recalculated for the field strength of 2 T and by comparing the chemical shifts with the shifts in the single compound spectra. The ratios of main metabolites, such as NAA/Cr, Cho/Cr, Cho~NAA and mI/Cr, are in accord with those obtained earlier using the software supplied with the MR imager and the absolute concentrations of N-acetylaspartate (NAA), choline containing compounds (Cho), myolnositol (mI), glucose (Glc) and glutamate (Glu) obtained from the tit agree with those reported in literature, which confirms the usefulness ot" the second derivative method in routine analyses of~Ft MR brain spectra. ~3
Journal of Magnetic Resonance, 2001
We have previously shown the continuous wavelet transform (CWT), a signal-processing tool, which is based upon an iterative algorithm using a lorentzian signal model, to be useful as a postacquisition water suppression technique. To further exploit this tool we show its usefulness in accurately quantifying the signal metabolites after water removal. However, due to the static field inhomogeneities, eddy currents, and "radiation damping," the water signal and the metabolites may no longer have a lorentzian lineshape. Therefore, another signal model must be used. As the CWT is a flexible method, we have developed a new algorithm using a gaussian model and found that it fits the signal components, especially the water resonance, better than the lorentzian model in most cases. A new framework, which uses the two models, is proposed. The framework iteratively extracts each resonance, starting by the water peak, from the raw signal and adjusts its envelope to both the lorentzian and the gaussian models. The model giving the best fit is selected. As a consequence, the small signals originating from metabolites when selecting, removing, and quantifying the dominant water resonance from the raw time domain signal are preserved and an accurate estimation of their concentrations is obtained. This is demonstrated by analyzing (1 H) magnetic resonance spectroscopy unsuppressed water data collected from a phantom with known concentrations at two different field strengths and data collected from normal volunteers using two different localization methods.