This paper describes a new quantitation method called AQSES for short echo time magnetic resonance spectra. This method is embedded in a software package available online from www.esat.kuleuven.be/sista/members/biomed/new/ with a graphical user interface, under an open source license, which means that the source code is freely available and easy to adapt to specific needs of the user. The quantitation problem is mathematically formulated as a separable nonlinear least-squares fitting problem, which is numerically solved using a modified variable-projection procedure. A macromolecular baseline is incorporated into the fit via nonparametric modelling, efficiently implemented using penalized splines. Unwanted components such as residual water are removed with a maximum-phase FIR filter. Constraints on the phases, dampings and frequencies of the metabolites can be imposed. AQSES has been tested on simulated MR spectra with several types of disturbance and on short echo time in vivo proton MR spectra. Results show that AQSES is robust, easy to use and very flexible.

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This study examines the effect of feature extraction methods prior to automated pattern recognition based on magnetic resonance spectroscopy (MRS) for brain tumor diagnosis. Since individual inspection of spectra is time-consuming and requires specific spectroscopic expertise, the introduction of clinical decision support systems (DSSs) is expected to strongly promote the clinical use of MRS. This study focuses on the feature extraction step in the preprocessing protocol of MRS when using a DSS. On two independent data sets, encompassing single-voxel and multi-voxel data, it is observed that the use of the full spectra together with a kernel-based technique, handling high dimensional data, or using an automated pattern recognition method based on independent component analysis or Relief-F achieves accurate performances. In addition, these approaches have low cost and are easy to automate. When sophisticated quantification methods are used in a DSS, user interaction should be minimized. The computationally intensive quantification techniques do not tend to increase the performance in these circumstances. The results suggest to simplify the feature reduction step in the preprocessing protocol when using a DSS purely for classification purposes. This can greatly speed up the execution of classifiers and DSSs and may accelerate their introduction into clinical practice.Magn Reson Med 60:288-298, 2008.

Magnetic resonance spectroscopy (MRS) is an effective diagnostic technique for monitoring biochemical changes in an organism. The lineshape of MRS signals can deviate from the theoretical Lorentzian lineshape due to inhomogeneities of the magnetic field applied to patients and to tissue heterogeneity. We call this deviation a distortion and study the self-deconvolution method for automatic estimation of the unknown lineshape distortion. The method is embedded within a time-domain metabolite quantitation algorithm for short-echo-time MRS signals. Monte Carlo simulations are used to analyze whether estimation of the unknown lineshape can improve the overall quantitation result. We use a signal with eight metabolic components inspired by typical MRS signals from healthy human brain and allocate special attention to the step of denoising and spike removal in the self-deconvolution technique. To this end, we compare several modeling techniques, based on complex damped exponentials, splines and wavelets. Our results show that self-deconvolution performs well, provided that some unavoidable hyper-parameters of the denoising methods are well chosen. Comparison of the first and last iterations shows an improvement when considering iterations instead of a single step of self-deconvolution.

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