GRAD) FIELDS J. W. C. van der Veen et al., "Accurate Quantification of in Vivo 31PNMR Signals Using the Variable Projec tion Method and Prior Knowledge, Magnetic Reso nance in Medicine', vol. 6, No. 1, Jan. 1988, pp. 92-98. H. Barkhuisjen et al., “Improved Algorithm for Nonin teractive Time-Domain Model Fitting to Exponentially Damped Magnetic Resonance Signals', Journal of Magnetic Resonance 73, pp. 553-557, 1987. C. H. Sotak et al., "Automatic Phase Correction of Fourier Transform NMR Spectra Based on the Disper sion Versus Absorption (DISPA) Lineshape Analysis', Journal of Magnetic Resonance 57, pp. 453-962, 1984.

This article describes the use of time-domain processing for analyzing spectra acquired in vivo. The theoretical basis of processing in the time domain is presented, emphasizing that this is also the acquisition domain. Some of the drawbacks of using the Fourier transform (FT) and analyzing data in the frequency domain are presented. A brief historical perspective is provided and algorithms are described for time-domain processing. The importance of accurate prior knowledge and calculating the limits of error estimation (Cramer Rao Bounds) are emphasized. The generation of prior knowledge using quantum- mechanical calculations of spin systems, enabling a wide range of pulse sequences and timing parameters to be simulated, is described. Problems associated with estimating background signals, such as from lipids, macromolecules, and membrane components, are considered and the theoretical breakdown of the Cramer Rao assumptions, if these are modelled nonparametrically, is discussed. Methods to mitigate these consequences, such as Monte-Carlo simulation and Bayesian estimation, are briefly considered. The jMRUI suite of software, featuring AMARES, QUEST, and AQSES, is used throughout to illustrate the steps involved, and practical examples of processing 31P magnetic resonance spectroscopy kinetic data and short echo brain H-1 spectra are provided.

MRI-scanners enable non-invasive, in vivo quantitation of metabolites in, e.g., the brain of a patient. Among other things, this requires adequate estimation of the unknown temporal decay function of the complex-valued signal emanating from the metabolites. We propose a method to render a current decay estimator more simple, accurate, and robust, and test it on a simulated signal comprising contributions from ten metabolite species and scanner noise.

The main subject of this work is the in vivo quantification of the metabolites concentrations revealed in the magnetic resonance spectroscopy (MRS) spectra. For this purpose, a novel two-stage processing methodology, consisting of the denoising of the MRS signal and the quantification of the metabolites' peaks using a genetic algorithm (GA), is proposed. The denoising stage tends to improve the quality of the acquired MRS signal in a way that makes the fitting procedure performed by the genetic algorithm (GA) more successful. Two different approaches for improving the MRS signal quality, the denoising via wavelet analysis and signal separation by singular value decomposition (SVD), under possible combinations are examined. The introduced quantification technique deals with metabolites' peaks overlapping, a considerably difficult situation occurred in real conditions. Extensive experiments have proved the efficiency of the introduced methodology in artificial MRS data by establishing it as a generic metabolite quantification procedure.

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. Magn Reson Med, 2013. © 2012 Wiley Periodicals, Inc.

Introduction: Glutamate and γ-aminobutyric acid (GABA) are the primary excitatory and inhibitory neurotransmitters in the CNS, respectively. Both are believed to be involved in a variety of psychiatric and neurological disorders. GABA can be measured using proton MRS with a PRESS-based two step editing sequence (1). Usually, glutamate or GLX (GLU + GLN) is measured in a separate scan using glutamate editing or short-TE methods. Due to the time constraint of many clinical studies, it is highly desirable to acquire both glutamate and GABA in a single scan. At 3 Tesla, separation of glutamate from Glx is usually difficult because of spectral overlap and strong J couplings. Here we used full density matrix simulation to investigate the effects of a spectral editing pulse used in GABA detection at 3 Tesla (1) on the J evolution of glutamate, glutamine and NAA. It was found that the GABA editing pulse (1), which also irradiates the glutamate and glutamine H3 protons, causes a significant spectral separation between glutamate H4 and glutamine H4 resonances. The contribution to the resonances in the 2.2-2.4 ppm region from the aspartyl moiety of NAA is also reduced at the relatively long echo time used in GABA editing. Our results showed that it is possible to extract glutamate signal using linear combination spectral fitting of the GABA spectra.

In this work we report on generating/using simulated metabolite basis sets for the quantification of in vivo MRS signals, assuming that they have been acquired by using the PRESS pulse sequence. To that end we have employed the classes and functions of the GAMMA C++ library. By using several versions of our PRESS-simulation program, we were able to study the single-voxel selection, required for detecting in vivo MRS signals. Furthermore, by introducing in one of the versions a modified spatial summation scheme, that comes down to crusher-gradient averaging, we could realize a decrease in computation time by about a factor of 256. We have used four different simulated metabolite basis sets in the quantification of a real-world 3T human-brain 1H MRS signal. The best quantification is obtained, when including into the simulation program -as closely as possible- the related details of the PRESS-based single-voxel selection

High-resolution magic angle spinning (HRMAS) Nuclear magnetic resonance (NMR) 1H spectroscopy is playing an increasingly important role for diagnosis. This technique enables setting up metabolite profiles of ex vivo pathological and healthy tissue. Automatic quantitation of HRMAS signals provides reliable reference profiles useful to monitor diseases and pharmaceutical follow-up. However for several metabolites, the values of chemical shifts of proton groups may slightly differ according to the microenvironment in the tissue or cells, in particular to its pH. This hampers accurate estimation of the metabolite concentrations mainly when using quantitation algorithms based on a metabolite basis-set: the metabolite fingerprints are not correct anymore. In this work, we propose an accurate method based on quantum mechanical (QM) simulations. The proposed algorithm automatically corrects mismatches between the signal under analysis and the signals of the simulated basic-set by modifying the basis-set signals. In the optimization procedure, the basis-set signals are simulated again by varying the chemical shifts of metabolites in the QM procedure. Cross-correlation was used as cost function to measure how well the signals match each other. The proposed method, QM-QUEST, provides more robust fitting while limiting user involvement and respects the correct fingerprints of metabolites. Its efficiency is demonstrated by accurately quantitating signals from tissue samples of human brains with oligodendroglioma. (C) 2011 Elsevier Masson SAS. All rights reserved.

A. Lazariev, A-R. Allouche, M. Aubert-Frécon, F. Fauvelle, K. Elbayed, M. Piotto, I. J. Namer, D. van Ormondt, and D. Graveron-Demilly Creatis-LRMN, Université Claude Bernard Lyon 1, Villeurbanne, France, LASIM, Université Claude Bernard Lyon 1, Villeurbanne, France, CRSSA/BCM, Grenoble, France, Institut de Chimie, Strasbourg, France, Bruker BioSpin, Wissembourg, France, Department of Biophysics and Nuclear Medicine, University Hospitals of, Strasbourg, France, Delft University of Technology, Delft, Netherlands

This work is related to the FAST jMRUI signal-analysis package. Recently this package has been refactored as a plug-in platform, allowing end-users to add their own features. Here we describe the creation of a jMRUI custom plug-in named Semipar. This plug-in integrates into jMRUI a two-NLLS criterion that we have recently developed for handling the quantitation of in vivo MRS signals with an unknown common lineshape. The Semipar plug-in was tested by applying it to a simulated MRS signal. This signal was generated by using the various methods and tools of the jMRUI platform. It was found that-depending on the SNR of the signal-the Semipar approach can improve the MRS quantitation results.

The in vivo quantification of metabolites' concentrations, revealed in magnetic resonance spectroscopy (MRS) spectra, constitutes the main subject under investigation in this work. Significant contributions based on artificial intelligence tools, such as neural networks (NNs), with good results have been presented lately but have shown several drawbacks, regarding their quantification accuracy under difficult conditions. A general framework that encounters the quantification procedure as an optimization problem, which is solved using a genetic algorithm (GA), is proposed in this paper. Two different lineshape models are examined, while two GA configurations are applied on artificial data. Moreover, the introduced quantification technique deals with metabolite peaks' overlapping, a considerably difficult situation occurring under real conditions. Appropriate experiments have proved the efficiency of the introduced methodology, in artificial MRS data, by establishing it as a generic metabolite quantification procedure.

High-resolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR) is playing an increasingly important role for diagnosis. This technique enables setting up metabolite profiles of ex vivo pathological and healthy tissue. The need to monitor diseases and pharmaceutical follow-up requires an automatic quantitation of HRMAS 1H signals. However, for several metabolites, the values of chemical shifts of proton groups may slightly differ according to the micro-environment in the tissue or cells, in particular to its pH. This hampers the accurate estimation of the metabolite concentrations mainly when using quantitation algorithms based on a metabolite basis set: the metabolite fingerprints are not correct anymore. In this work, we propose an accurate method coupling quantum mechanical simulations and quantitation algorithms to handle basis-set changes. The proposed algorithm automatically corrects mismatches between the signals of the simulated basis set and the signal under analysis by maximizing the normalized cross-correlation between the mentioned signals. Optimized chemical shift values of the metabolites are obtained. This method, QM-QUEST, provides more robust fitting while limiting user involvement and respects the correct fingerprints of metabolites. Its efficiency is demonstrated by accurately quantitating 33 signals from tissue samples of human brains with oligodendroglioma, obtained at 11.7 tesla. The corresponding chemical shift changes of several metabolites within the series are also analyzed.

Magnetic resonance spectroscopy (MRS) is the method of choice for noninvasive in vivo measurement of metabolites in patients. When the model function describing the acquired MRS signal is incomplete, semi-parametric techniques are required for estimation of the wanted metabolite concentrations. In this work, incompleteness means that the model function of the MRS signal decay is unknown. We devised the simplest method yet for avoiding cumbersome searches in function space, attendant on semi-parametric estimation. This is based on the assumption that all sinusoids in the MRS signal have equal decay and that this decay has no high-frequency components. Application of the method through a plug-in for the metabolite quantitation software package jMRUI is envisioned.

Chemical shifts δ have been calculated for the 1H attached to carbon atoms of sarcosine. Eight levels of theory within the DFT approach were used, mixing the four functionals B3LYP, PBE, OPBE, PBE0 and the two basis sets 6-311++G∗∗ and pcJ2. Boltzmann weighted isomer effects have been evaluated. By comparison of the 1H NMR spectrum simulated from the calculated δ and the experimental one that we acquired at 300MHz, the B3LYP/6-311++G∗∗ calculation was seen to be a good compromise between accuracy and cost. Zero-point vibrational corrections, estimated using a second-order perturbation approach, increase the agreement with experiment.