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Biomedical subjects

D van Ormondt

Publications and source records attributed to D van Ormondt.

14 recordsLinked to original sources
PubMed2002

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. In the context of a European Union Training and Mobility of Researchers project we have decided to integrate all methods into one coordinating software system. This system meets the requirements that it is highly structured in an object-oriented manner using the Unified Modeling Language and the Java programming environment, that it uses the client-server approach, that it allows multi-client communication sessions with facilities for sharing data and that it is a true distributed computing system with guaranteed reliability using core activities of the Java Jini package.

Algorithms↗
PubMed2001

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. Within each section, articles are listed in alphabetical order with respect to author. If, in the preceding period, no publications are located relevant to any one of these headings, that section will be omitted.

Magnetic Resonance Spectroscopy↗
PubMed2001

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. 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. Abbreviations used: CRB Cramér-Rao lower bounds

Animals↗
PubMed1999

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. We show that this approach yields statistically better results than processing the individual signals separately.

Adenosine Triphosphate↗
PubMed1998

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. The proposed method is based on applying the principle of signal superposition and on using the consequences of the sampling principle. The method works in the time domain. It is very simple and rapid and does not depend on the properties of the acquired signal or reconstruction algorithm. It will be shown and explained in which cases the transient response can be neglected and in which it has to be compensated. In the end, the results of the proposed methods will be shown for mentioned cases on a simulated signal in the image domain.

Image Processing, Computer-Assisted↗
PubMed1996

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. This was achieved by varying the number of scans summed prior to analysis. Since T2 relaxation in glycogen has been shown to be multiexponential [Overloop, K. et al. Magn. Reson. Med. 36, 45-51 (1996], the exact quantification of the C-1 glycogen signal requires a model function comprising a sum of Lorentzian components, each with a different broadening at the glycogen frequency. This paper focuses on the performances of the above methods to fit such a multicomponent resonance line. In the frequency domain, line fitting with two Lorentz lines gives good results at sufficiently high S/N. In the time domain, VARPRO performs better than HTLS because fixed values can be imposed to the linewidth of the components at the common C-1 frequency, thereby reducing convergence problems at low S/N.

Algorithms↗
PubMed1993

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. Furthermore, reduction in computational time of the image reconstructions could be realized by using information provided by a neural network classification of the spectra, calculated from the MRSI data sets.

Artifacts↗
PubMed1992

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. It is shown how prior knowledge is mathematically incorporated in the Gauss-Newton method. From proton NMR measurements of model solutions actual prior knowledge is extracted. With this knowledge relative concentrations are determined from a mixture of metabolites. The fitted results agree with the true values within the margins of the noise. After some minor changes the same prior knowledge was successfully used to analyze a series of in vivo rat brain measurements.

Magnetic Resonance Spectroscopy↗
PubMed1989

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. Significant spectral variations are observed within both populations of normal brain and of intracranial tumor 31P MR spectra. The metabolic ratios derived from the glioma 31P MR spectra and from corresponding uninfiltrated brain tissue do not differ significantly. Significant differences are, however, observed between the metabolic ratios of prolactinoma and uninfiltrated tissue 31P MR spectra. Alkaline pH values are found for the prolactinoma and the high-grade gliomas. Furthermore, spectral differences are observed between the patient's uninfiltrated tissue 31P MR spectra and those of an unmatched population of volunteers. This underscores the necessity for control measurements on the uninfiltrated tissue of the patient and for controls from a matched population of healthy individuals.

Adult↗
PubMed1985

Application of linear prediction and singular value decomposition (LPSVD) to determine NMR frequencies and intensities from the FID.

A parametric spectral analysis based on linear prediction and singular value decomposition has been applied to the 31P FID of a tumor implanted in a mouse. It is found that a number of complications that may arise in in vivo FFT NMR can be circumvented. The new method yields all parameters of the spectral components, i.e., the frequency, amplitude, damping constant, and phase.

Animals↗
PubMedDate not supplied

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. Third, in order to save computational time, interpolation of the metabolite images (from size 32 x 32 to 128 x 128) was performed in the image-domain by applying one-dimensional cubic splines. It was found that the residual water signals can be almost completely removed from the SI data sets by applying the linear HSVD fitting method. Furthermore, it was found that voxel dependency of certain NMR parameters (e.g., variations of the spin-echo offset frequencies and/or phase factors) can be accounted for automatically by applying the nonlinear time-domain fitting technique. For that purpose it appeared to be essential to employ prior knowledge of the NMR spectral parameters.

Aspartic Acid↗