Biomedical subjects

J Sijbers

Publications and source records attributed to J Sijbers.

16 recordsLinked to original sources

PubMed2005

Mathematical framework for simulating diffusion tensor MR neural fiber bundles.

White matter (WM) fiber tractography (i.e., the reconstruction of the 3D architecture of WM fiber pathways) is known to be an important application of diffusion tensor magnetic resonance imaging (DT-MRI). For the quantitative evaluation of several fiber-tracking properties, such as accuracy, noise sensitivity, and robustness, synthetic ground-truth DT-MRI data are required. Moreover, an accurate simulated phantom is also required for optimization of the user-defined tractography parameters, and objective comparisons between fiber-tracking algorithms. Therefore, in this study a mathematical framework for simulating DT-MRI data, based on the physical properties of WM fiber bundles, is presented. We obtained a model of a WM fiber bundle by parameterizing the various features that characterize this bundle. We then evaluated three different synthetic DT-MRI models using experimental data in order to test the proposed methodology, and to determine the optimum model and parameter settings for constructing a realistic simulated DT-MRI phantom. Several examples of how the mathematical framework can be applied to compare fiber-tracking algorithms are presented.

Algorithms↗

PubMed2005

Generalized likelihood ratio tests for complex fMRI data: a simulation study.

Statistical tests developed for the analysis of (intrinsically complex valued) functional magnetic resonance time series, are generally applied to the data's magnitude components. However, during the past five years, new tests were developed that incorporate the complex nature of fMRI data. In particular, a generalized likelihood ratio test (GLRT) was proposed based on a constant phase model. In this work, we evaluate the sensitivity of GLRTs for complex data to small misspecifications of the phase model by means of simulation experiments. It is argued that, in practical situations, GLRTs based on magnitude data are likely to perform better compared to GLRTs based on complex data in terms of detection rate and constant false alarm rate properties.

Algorithms↗

PubMed2004

Maximum likelihood estimation of signal amplitude and noise variance from MR data.

In MRI, the raw data, which are acquired in spatial frequency space, are intrinsically complex valued and corrupted by Gaussian-distributed noise. After applying an inverse Fourier transform, the data remain complex valued and Gaussian distributed. If the signal amplitude is to be estimated, one has two options. It can be estimated directly from the complex valued data set, or one can first perform a magnitude operation on this data set, which changes the distribution of the data from Gaussian to Rician, and estimate the signal amplitude from the obtained magnitude image. Similarly, the noise variance can be estimated from both the complex and magnitude data sets. This article addresses the question whether it is better to use complex valued data or magnitude data for the estimation of these parameters using the maximum likelihood method. As a performance criterion, the mean-squared error (MSE) is used.

Algorithms↗

PubMed2002

An energy-based beam hardening model in tomography.

As a consequence of the polychromatic x-ray source, used in micro-computer tomography (microCT) and in medical CT, the attenuation is no longer a linear function of absorber thickness. If this nonlinear beam hardening effect is not compensated, the reconstructed images will be corrupted by cupping artefacts. In this paper, a bimodal energy model for the detected energy spectrum is presented, which can be used for reduction of artefacts caused by beam hardening in well-specified conditions. Based on the combination of the spectrum of the source and the detector efficiency, the assumption is made that there are two dominant energies which can describe the system. The validity of the proposed model is examined by fitting the model to the experimental datapoints obtained on a microtomograph for different materials and source voltages.

Algorithms↗

PubMed2000

Automatic localization of EEG electrode markers within 3D MR data.

The electrical activity of the brain can be monitored using ElectroEncephaloGraphy (EEG). From the positions of the EEG electrodes, it is possible to localize focal brain activity. Thereby, the accuracy of the localization strongly depends on the accuracy with which the positions of the electrodes can be determined. In this work, we present an automatic, simple, and accurate scheme that detects EEG electrode markers from 3D MR data of the human head.

Electrodes↗

PubMed2000

Special designed RF-antenna with integrated non-invasive carbon electrodes for simultaneous magnetic resonance imaging and electroencephalography acquisition at 7T.

The construction of a high quality MR RF-antenna with incorporated EEG electrodes for simultaneous MRI and EEG acquisition is presented. The antenna comprises an active decoupled surface coil for receiving the MR signal and a whole body coil for transmitting the excitation RF pulses. The surface coil offers a high signal-to-noise ratio required for fMRI application and the whole body coil has a good B(1) excitation profile, which enables the application of homogeneous RF pulses. Non-invasive carbon electrodes are used in order to minimise the magnetic susceptibility artefacts that occur upon application of conductive materials. This dedicated set-up is compared to a standard set-up being a linear birdcage coil and commercially available Ag/AgCl electrodes. As the acquired EEG signals are heavily disturbed by the gradient switching, intelligent filtering is applied to obtain a clean EEG signal.

Animals↗

PubMed1999

Adaptive anisotropic noise filtering for magnitude MR data.

Conventional noise filtering schemes applied to magnitude magnetic resonance (MR) images tacitly assume Gauss distributed noise. Magnitude MR data, however, are Rice distributed. Not incorporating this knowledge leads inevitably to biased results, in particular when applying such filters in regions with low signal-to-noise ratio. In this work, we show how the Rice data probability distribution can be incorporated so as to construct a noise filter that is far less biased.

Anisotropy↗

PubMed1999

Restoration of MR-induced artifacts in simultaneously recorded MR/EEG data.

During a Magnetic Resonance sequence, simultaneously acquired ElectroEncephaloGraphy (EEG) data are compromised by severe pollution due to artifacts originating from the switching of the magnetic field gradients. In this work, it is shown how these artifacts can be strongly reduced or even removed through application of an adaptive artifact restoration scheme. The method has proved to be fully automatic and to retain high frequency EEG information, which is indispensable for many EEG applications.

Animals↗

PubMed1999

Neuroanatomy of the fragile X knockout mouse brain studied using in vivo high resolution magnetic resonance imaging.

Magnetic resonance imaging (MRI) of the brain of fragile X patients, the most frequent form of inherited mental retardation, has revealed abnormalities in the size of specific brain structures, including the cerebellar vermis, the hippocampus, and the ventricular system. We intended to quantify the differences observed in the patient studies in the fragile X knockout mouse model, which is a good model for the disease, paralleling the human disorder in having cognitive deficits, macro-orchidism, and immature dendritic spines. Therefore we set up MRI of the mouse brain which allowed us to measure the size of the brain structures reported to be abnormal in human fragile X patients in the mouse model. We did not find evidence for size alterations in various brain regions of the fragile X mouse model, but the method described may find a wide application in the study of mutant mouse models with neurological involvement.

Animals↗

PubMed1999

How to optimize the design of a quantitative HREM experiment so as to attain the highest precision.

Using parameter estimation theory, an expression is derived for the maximum precision with which the position of a single atom can be estimated from high resolution transmission electron microscopy images. This expression, being a complicated function of the object as well as of the microscope parameters and the electron dose, can be used to optimize the design of an HREM experiment so as to attain the highest precision.

Journal Article↗

PubMed1998

Imaging birds in a bird cage: in-vivo FSE 3D MRI of bird brain.

An in-vivo magnetic resonance imaging (MRI) procedure is described that allows one to obtain three-dimensional high quality images of the entire brain of small birds such as the canary (20 g) and the starling (75 g) with an image resolution of 0.1 mm (58-113 microm, dependent on the size of the imaged bird). The entire imaging procedure took about 2 h after which the birds recovered from anaesthesia uneventfully and could be reused for subsequent additional imaging. This non invasive MRI technique enables to correlate brain measures with behavioural or physiological data that are dynamic in nature and could permit significant progress for bird neurological research.

Animals↗

PubMed1998

Estimation of the noise in magnitude MR images.

Magnitude magnetic resonance data are Rician distributed. In this note a new method is proposed to estimate the image noise variance for this type of data distribution. The method is based on a double image acquisition, thereby exploiting the knowledge of the Rice distribution moments.

Magnetic Resonance Imaging↗

PubMed1998

L1 knockout mice show dilated ventricles, vermis hypoplasia and impaired exploration patterns.

L1 is a neural cell adhesion molecule mainly involved in axon guidance and neuronal migration during brain development. Mutations in the human L1 gene give rise to a complex clinical picture, with mental retardation, neurologic abnormalities and a variable degree of hydrocephalus. Recently, a transgenic mouse model with a targeted null mutation in the L1 gene was generated. These knockout (KO) mice show hypoplasia of the corticospinal tract. Here we have performed further studies of these KO mice including magnetic resonance imaging of the brain, neuropathological analysis and behavioral testing. The ventricular system was shown to be abnormal with dilatation of the lateral ventricles and the 4th ventricle, and an altered shape of the Sylvius aqueduct. Additionally, the cerebellar vermis of the KO mice is hypoplastic. Their exploratory behavior is characterized by stereotype peripheral circling reminiscent of that of rodents with induced cerebellar lesions.

Animals↗

PubMed1998

Maximum-likelihood estimation of Rician distribution parameters.

The problem of parameter estimation from Rician distributed data (e.g., magnitude magnetic resonance images) is addressed. The properties of conventional estimation methods are discussed and compared to maximum-likelihood (ML) estimation which is known to yield optimal results asymptotically. In contrast to previously proposed methods, ML estimation is demonstrated to be unbiased for high signal-to-noise ratio (SNR) and to yield physical relevant results for low SNR.

Computer Simulation↗

PubMed1997

Watershed-based segmentation of 3D MR data for volume quantization.

The aim of this work is the development of a semiautomatic segmentation technique for efficient and accurate volume quantization of Magnetic Resonance (MR) data. The proposed technique uses a 3D variant of Vincent and Soilles immersion-based watershed algorithm that is applied to the gradient magnitude of the MR data and that produces small volume primitives. The known drawback of the watershed algorithm, oversegmentation, is strongly reduced by a priori application of a 3D adaptive anisotropic diffusion filter to the MR data. Furthermore, oversegmentation is a posteriori reduced by properly merging small volume primitives that have similar gray level distributions. The outcome of the proceeding image processing steps is presented to the user for manual segmentation. Through selection of volume primitives, the user quickly segments of first slice, which contains the object of interest. Afterwards, the subsequent slices are automatically segmented by extrapolation. Segmentation results are contingently manually corrected. The proposed segmentation technique is tested on phantom objects, where segmentation errors less than 2% are observed. In addition, the technique is demonstrated on 3D MR data of the mouse head from which the cerebellum is extracted. Volumes of the mouse cerebellum and the mouse brains in toto are calculated.

Algorithms↗

PubMed1996

Quantification and improvement of the signal-to-noise ratio in a magnetic resonance image acquisition procedure.

A procedure is developed to quantify and improve the signal-to-noise ratio (SNR) of magnetic resonance images. The image SNR is quantified using the correlation function of two independent acquisitions of an image. To test the performance of the quantification, SNR measurement data are fitted to theoretically expected curves. The proposed correlation technique is also used to improve the SNR by estimating the amplitude of the signal spectrum. The technique is applied to a set of MR images, and its performance in terms of gain in SNR, contrast-to-noise ratio (CNR), and resolution loss is compared to that of classical noise filters. The SNR as well as the CNR is improved significantly with minor loss of resolution. Finally, it is shown that the correlation technique can be implemented in a highly efficient way in almost any acquisition procedure of a magnetic resonance imaging system.

Image Processing, Computer-Assisted↗