Optimized quantitative magnetic resonance spectroscopy for clinical routine.

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.

Quantitation with QUEST of brain HRMAS-NMR signals: application to metabolic disorders in experimental epileptic seizures.

Quantitation of High Resolution Magic Angle Spinning (HRMAS) Nuclear Magnetic Resonance (NMR) signals enables establishing reference metabolite profiles of ex vivo tissues. Signals are often contaminated by a background signal originating mainly from macromolecules and lipids and by residual water which hampers proper quantitation. We show that automatic quantitation of HRMAS signals, even in the presence of a background, can be achieved by the semi-parametric algorithm QUEST based on prior knowledge of a metabolite basis-set.

Influence of measured and simulated basis sets on metabolite concentration estimates.

By quantification of brain metabolites, localized brain proton MRS can non-invasively provide biochemical information from distinct regions of the brain. Quantification of short-TE signals is usually based on a metabolite basis set. The basis set can be obtained by two approaches: (1) by measuring the signals of metabolites in aqueous solution; (2) by quantum-mechanically simulating the theoretical metabolite signals.

Semi-parametric estimation in magnetic resonance spectroscopy: automation of the disentanglement procedure.

Semi-parametric disentanglement of parametric parts from non-parametric parts of a signal is a universal problem. This study concerns estimation of metabolite concentrations from in vivo Magnetic Resonance Spectroscopy (MRS) signals. Due to in vivo conditions, so-called macro-molecules contribute non-parametric components to the signals.

Time-domain semi-parametric estimation based on a metabolite basis set.

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.

Time-domain quantitation of 1H short echo-time signals: background accommodation.

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.

A distributed computing system for magnetic resonance imaging: Java-based processing and binding of XML.

Recently we have developed a Java-based heterogeneous distributed computing system for the field of magnetic resonance imaging (MRI). It is a software system for embedding the various image reconstruction algorithms that we have created for handling MRI data sets with sparse sampling distributions. Since these data sets may result from multi-dimensional MRI measurements our system has to control the storage and manipulation of large amounts of data.

Retrospective intra-scan motion correction.

This paper analyzes the effects of intra-scan motion and demonstrates the possibility of correcting them directly in k-space with a new automatic retrospective method. The method is presented for series of 2D acquisitions with Cartesian sampling. Using a reference k-space acquisition (corrected for translations) within the series, intra-scan motion parameters are accurately estimated for each trajectory in k-space of each data set in the series resulting in pseudo-random sample positions.

Dynamic magnetic resonance imaging paragraph sign with radial scanning: a post-acquisition paragraph sign keyhole approach.

A method - PA-keyhole - for 2D/3D dynamic magnetic resonance imaging with radial scanning is proposed. PA-keyhole exploits the inherent strong oversampling in the center of k-space, which contains crucial temporal information regarding contrast evolution. The method is based on: (1).

MR image reconstruction algorithms for sparse k-space data: a Java-based integration.

We have worked on multi-dimensional magnetic resonance imaging (MRI) data acquisition and related image reconstruction methods that aim at reducing the MRI scan time. To achieve this scan-time reduction we have combined the approach of 'increasing the speed' of k-space acquisition with that of 'deliberately omitting' acquisition of k-space trajectories (sparse sampling). Today we have a whole range of (sparse) sampling distributions and related reconstruction methods.

Current awareness.

In order to keep subscribers up-to-date with the latest developments in their field, John Wiley & Sons are providing a current awareness service in each issue of the journal. The bibliography contains newly published material in the field of NMR in biomedicine. Each bibliography is divided into 9 sections: 1 Books, Reviews ' Symposia; 2 General; 3 Technology; 4 Brain and Nerves; 5 Neuropathology; 6 Cancer; 7 Cardiac, Vascular and Respiratory Systems; 8 Liver, Kidney and Other Organs; 9 Muscle and Orthopaedic.

Cramér-Rao bounds: an evaluation tool for quantitation.

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.

Cramer-Rao bound expressions for parametric estimation of overlapping peaks: influence of prior knowledge.

We have derived analytical expressions of the Cramer-Rao lower bounds on spectral parameters for singlet, doublet, and triplet peaks in noise. We considered exponential damping (Lorentzian lineshape) and white Gaussian noise. The expressions, valid if a sufficiently large number of samples is used, were derived in the time domain for algebraic convenience.

Time-domain quantification of series of biomedical magnetic resonance spectroscopy signals.

Quantification of individual magnetic resonance spectroscopy (MRS) signals is possible in the time domain using interactive nonlinear least-squares fitting methods which provide maximum likelihood parameter estimates under certain assumptions or using fully automatic, but statistically suboptimal, black-box methods. In kinetic experiments time series of consecutive MRS spectra are measured in which information concerning the time evolution of some of the signal parameters is often present. The purpose of this paper is to show how AMARES, a representative example of the interactive methods, can be extended to the simultaneous processing of all spectra in the time series using the common information present in the spectra.

The beneficial influence of prior knowledge on the quantitation of in vivo magnetic resonance spectroscopy signals.

Rationale And Objectives: This work concerns quantitation of in vivo magnetic resonance spectroscopy signals and the influence of prior knowledge on the precision of parameter estimates. The authors point out how prior knowledge can be used for experiments.

Methods: The Cramer-Rao lower bounds formulae of the noise-related standard deviations on spectral parameters for doublets and triplets were derived.

Influence of digital audio filters on image reconstruction in MRI.

This paper deals with the influence of the transient response and group delay of digital filters on the MRI signal and its aspects in image reconstruction. The consequence of digital filtration on the acquired signal will be shown in the time domain (k-space) for three basic imaging methods-echo scan, radial scan and spiral scan. The influence of the group delay and transient response of filters will be explained and a method will be proposed which compensates both these phenomena while retaining all the advantages of digital filtration.

Frequency-selective MRS data quantification with frequency prior knowledge.

Various signal processing techniques have been proposed to improve spectral estimation of closely spaced sinusoids in the presence of noise. This paper exploits frequency prior knowledge information to extract single peaks in magnetic resonance spectra, corresponding to metabolites of interest, by means of a highly selective finite impulse response filter. Thereafter the estimation of the parameters of the peaks is carried out using a singular-value-decomposition-based method known as HTLS.

Evaluation of signal processing methods for the quantification of a multi-exponential signal: the glycogen 13C-1 NMR signal.

The 13C-1 NMR peak in proton-decoupled spectra of liver glycogen solution was quantitatively analyzed by three types of model-function fitting algorithms: iterative line-fitting in the frequency domain (MDCON); iterative least-squares fitting (VARPRO) in the time domain; and noniterative singular value decomposition-based analysis (HTLS), also in the time domain. Quantification results were compared with manual integration values. Performance of the algorithms was tested at different signal-to-noise ratios (S/N) of the glycogen C-1 peak.

Reduced lipid contamination in in vivo 1H MRSI using time-domain fitting and neural network classification.

It is a well-known problem that metabolite maps, reconstructed from in vivo 1H MRSI data sets, may suffer from contamination caused by the presence of strong lipid signals. In the present investigation, the lipid problem was addressed by applying specific signal processing and data-analysis techniques, combined with pattern recognition based on the concept of the artificial neural network. In order to arrive at images, cleaned from lipid artifacts, we have applied our previously introduced iterative and noniterative time-domain fitting procedures.

Determination of concentrations by time domain fitting of proton NMR echo signals using prior knowledge.

A fast and flexible time domain iterative fitting procedure that can be used to fit free induction decays as well as echo-like signals is described. Damping constants of the first and second part of the echo do not have to be identical. Prior knowledge can be used to diminish the number of parameters to be fitted, which results in an improved accuracy.

Application of time-domain fitting in the quantification of in vivo 1H spectroscopic imaging data sets.

Time-domain model function fitting techniques were applied to improve the reconstruction of metabolite maps from the data sets obtained from in vivo 1H spectroscopic imaging (SI) experiments. First, residual water-related signals were removed from the SI data sets by using SVD-based linear time-domain fitting based upon the HSVD (State Space) approach. Second, peak integrals of the metabolites of interest were obtained by quantifying the proton spin-echoes of the voxels by means of non-linear time-domain fitting based upon the maximum likelihood principle.

1H image-guided localized 31P MR spectroscopy of human brain: quantitative analysis of 31P MR spectra measured on volunteers and on intracranial tumor patients.

1H image-guided 31P MR spectra of normal human brain and of intracranial tumors have been analyzed quantitatively. Tumor types examined include prolactinoma, lymphoma, and various grade gliomas. The experimental signals were processed by means of a time-domain least-square fitting procedure, which yields the spectral parameters, as well as a prediction of the standard deviations.