The software package jMRUI with Java-based graphical user interface enables user-friendly time-domain analysis of magnetic resonance spectroscopy (MRS) and spectroscopic imaging (MRSI) and HRMAS-NMR signals. Version 3.x has been distributed in more than 1200 groups or hospitals worldwide. The new version 4.x is a plug-in platform enabling the users to add their own algorithms. Moreover, it offers new functionalities compared to versions 3.x. The quantum-mechanical simulator based on NMR-SCOPE, the quantitation algorithm QUEST and the main MRSI functionalities are described. Quantitation results of signals obtained in vivo from a mouse and a human brain are given.

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A novel and fast time-domain quantitation algorithm--quantitation based on semi-parametric quantum estimation (QUEST)--invoking optimal prior knowledge is proposed and tested. This nonlinear least-squares algorithm fits a time-domain model function, made up from a basis set of quantum-mechanically simulated whole-metabolite signals, to low-SNR in vivo data. A basis set of in vitro measured signals can be used too. The simulated basis set was created with the software package NMR-SCOPE which can invoke various experimental protocols. Quantitation of 1H short echo-time signals is often hampered by a background signal originating mainly from macromolecules and lipids. Here, we propose and compare three novel semi-parametric approaches to handle such signals in terms of bias-variance trade-off. The performances of our methods are evaluated through extensive Monte-Carlo studies. Uncertainty caused by the background is accounted for in the Cramér-Rao lower bounds calculation. Valuable insight about quantitation precision is obtained from the correlation matrices. Quantitation with QUEST of 1H in vitro data, 1H in vivo short echo-time and 31P human brain signals at 1.5 T, as well as 1H spectroscopic imaging data of human brain at 1.5 T, is demonstrated.

This article describes the Java-based version of the magnetic resonance user interface (MRUI) quantitation package. This package allows MR spectroscopists to easily perform time-domain analysis of in vivo MR spectroscopy data. We show that the Java programming language is very well suited for developing highly interactive graphical software applications such as the MRUI software. We have also established that MR quantitation algorithms, programmed in other languages, can easily be embedded into the Java-based MRUI by using the Java native interface (JNI). This new graphical user interface (GUI) has been conceived for the processing of large data sets and uses prior knowledge data-bases to make interactive quantitation algorithms more userfriendly.

The Cramér-Rao lower bounds (CRBs) are the lowest possible standard deviations of all unbiased model parameter estimates obtained from the data. Consequently they give insight into the potential performance of quantitation estimators. Using analytical CRB expressions for spectral parameters of singlets and doublets in noise, one is able to judge the precision as a function of spectral and experimental parameters. We point out the usefulness of these expressions for experimental design. The influence of constraints (chemical prior knowledge) on spectral parameters of the peaks of doublets is demonstrated and the inherent benefits for quantitation are shown.

Quantitation of 1H short echo-time signals is often hampered by a background signal originating mainly from macromolecules and lipids. While the model function of the metabolite signal is known, that of the macromolecules is only partially known. We present time-domain semi-parametric estimation approaches based on the QUEST quantitation algorithm (QUantitation based on QUantum ESTimation) and encompassing Cramér-Rao bounds that handle the influence of 'nuisance' parameters related to the background. Three novel methods for background accommodation are presented. They are based on the fast decay of the background signal in the time domain. After automatic estimation, the background signal can be automatically (1) subtracted from the raw data, (2) included in the basis set as multiple components, or (3) included in the basis set as a single entity. The performances of these methods combined with QUEST are evaluated through extensive Monte Carlo studies. They are compared in terms of bias-variance trade-off. Because error bars on the amplitudes are of paramount importance for diagnostic reliability, Cramér-Rao bounds accounting for the uncertainty caused by the background are proposed. Quantitation with QUEST of in vivo short echo-time (1)H human brain with estimation of the background is demonstrated.

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