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At least 1,063 records · Page 59Linked to original sources
PubMed2003

A simulation study of the error in dipole source localization for EEG spikes with a realistic head model.

OBJECTIVE: We tried to determine the error range of dipole modeling for EEG spikes originating from various clinically important sources by a simulation study employing a realistic head model. The computed error range was also compared with the degree of disturbance of dipole modeling caused by adding background activity to the spike. METHODS: The scalp fields generated by temporal, frontal and rolandic epileptic sources with spatial extent were simulated, and the corresponding 3-dimensional maps of residual variance (RV) were built by computing the RV for a single dipole at each point on a fine imaginary grid in the brain. Single dipole modeling was also performed for the simulated scalp fields after adding real background activity. RESULTS: The brain volume corresponding to a small RV was compact for the frontal sources and the lateral and baso-mesial temporal sources, and large for the anterior and baso-lateral temporal sources. The distribution of dipoles estimated for spikes contaminated with background corresponded to that of the volume of small RV and to spike-amplitude. Estimates were improved by employing inferior temporal electrodes. CONCLUSIONS: When evaluating dipole models of epileptic spikes, error ranges can be estimated and they vary considerably from region to region. SIGNIFICANCE: This study illustrates the variability of the error in dipole modeling of epileptic spikes. This variability is important when considering the clinical interpretation of modeling results.

Brain Mapping↗
PubMed2003

On the performances of noise filters in the restoration of oscillatory behavior in continuous yeast cultures.

Continuous flow microbial fermentations under industrial conditions are subject to the influx of noise, mainly through the feed stream. Noise upsets the normal deterministic behavior. For continuous cultures of Saccharomyces cerevisiae exhibiting oscillatory responses, four kinds of commonly used noise filters, three algorithmic and one neural, have been compared for their ability to restore noise-free oscillations. An auto-associative neural filter was the best, similar to earlier observations for other organisms under non-oscillatory conditions. This enhances the general applicability of neural filters for industrial scale fermentations.

Algorithms↗
PubMed2004

An adaptive way for improving noise reduction using local geometric projection.

We propose an adaptive way to improve noise reduction by local geometric projection. From the neighborhood of each candidate point in phase space, we identify the best subspace that the point will be orthogonally projected to. The signal subspace is formed by the most significant eigendirections of the neighborhood, while the less significant ones define the noise subspace. We provide a simple criterion to separate the most significant eigendirections from the less significant ones. This criterion is based on the maximum logarithmic difference between the neighborhood eigendirection lengths, and the assumption that there is at least one eigendirection that corresponds to the noise subspace. In this way, we take into account the special characteristics of each neighborhood and introduce a more successful noise reduction technique. Results are presented for a chaotic time series of the Henon map and Ikeda map, as well as on the Nasdaq Composite index.

Algorithms↗
PubMed2005

A new route to chaos: sequences of topological torus bifurcations.

We consider a sequence of topological torus bifurcations (TTBs) in a nonlinear, quasiperiodic Mathieu equation. The sequence of TTBs and an ensuing transition to chaos are observed by computing the principal Lyapunov exponent over a range of the bifurcation parameter. We also consider the effect of the sequence on the power spectrum before and after the transition to chaos. We then describe the topology of the set of knotted tori that are present before the transition to chaos. Following the transition, solutions evolve on strange attractors that have the topology of fractal braids in Poincare sections. We examine the topology of fractal braids and the dynamics of solutions that evolve on them. We end with a brief discussion of the number of TTBs in the cascade that leads to chaos.

Algorithms↗
PubMed2006

Modeling global vector fields of chaotic systems from noisy time series with the aid of structure-selection techniques.

We address the problem of reconstructing a set of nonlinear differential equations from chaotic time series. A method that combines the implicit Adams integration and the structure-selection technique of an error reduction ratio is proposed for system identification and corresponding parameter estimation of the model. The structure-selection technique identifies the significant terms from a pool of candidates of functional basis and determines the optimal model through orthogonal characteristics on data. The technique with the Adams integration algorithm makes the reconstruction available to data sampled with large time intervals. Numerical experiment on Lorenz and Rossler systems shows that the proposed strategy is effective in global vector field reconstruction from noisy time series.

Algorithms↗
PubMed1997

Implicit Kalman filtering.

For an implicitly defined discrete system, a new algorithm for Kalman filtering is developed and an efficient numerical implementation scheme is proposed. Unlike the traditional explicit approach, the implicit filter can be readily applied to ill-conditioned systems and allows for generalization to descriptor systems. The implementation of the implicit filter depends on the solution of the congruence matrix equation (A1)(Px)(AT1) = Py. We develop a general iterative method for the solution of this equation, and prove necessary and sufficient conditions for convergence. It is shown that when the system matrices of an implicit system are sparse, the implicit Kalman filter requires significantly less computer time and storage to implement as compared to the traditional explicit Kalman filter. Simulation results are presented to illustrate and substantiate the theoretical developments.

Algorithms↗
PubMedDate not supplied

QRS detection using new wavelets.

This paper deals with a new wavelet (WVT) which has been developed and very effectively and efficiently used for the detection of QRS segments from the ECG signal. After carrying out the detection using five existing wavelets (two symmetric--WT1 and WT2--and three asymmetric--WT3, WT4 and WT5), two new wavelets (WT6 and WT7) were constructed and used for QRS detection. WT6 is a symmetric wavelet and has been constructed by a trial-and-error method. WT7 is an adaptive symmetric wavelet and adjusts its threshold as per the amplitude of the ECG signal. The accuracy of QRS detection obtained from WT6 is 99.8 % and from WT7 100%. The CSE DS-3 database has been used for tests. Both WT6 and WT7 have been proved to be superior in performance to the existing wavelets. Out of WT6 and WT7, WT7 holds high promise for error-free reliable QRS detection in computer-aided feature extraction and disease diagnostics.

Algorithms↗
PubMedDate not supplied

Symbolic dynamics applied to optokinetic nystagmus signals.

In this study symbolic dynamics is used to analyse the time evolution of the optokinetic nystagmus (OKN). The inter-saccadic nystagmus differences are transformed into a sequence of three equally numbered letters (symbols) which represents the temporal changes in the inter-saccadic signal: (a) a fall in the temporal change, (b) no changes and (c) a rise in temporal change. The complexity of the data series was then calculated as the entropy of the word length three probability distribution of the symbol sequence. The method was applied to OKN signals from ten healthy subjects and ten patients suffering from vertigo (four tests on each subject) and to 40 artificial white noise data series of the same length as the symbolic representation of the OKN data. Applying Student's t-test showed a statistically significant lower mean entropy value (p<0.05) for the patients.

Adult↗
PubMedDate not supplied

Homomorphic wavelet thresholding technique for denoising medical ultrasound images.

A novel homomorphic wavelet thresholding technique for reducing speckle noise in medical ultrasound images is presented. First, we show that the speckle wavelet coefficients in the logarithmically transformed ultrasound images are best described by the Nakagami family of distributions. By exploiting this speckle model and the Laplacian signal prior, a closed form, data-driven, and spatially adaptive threshold is derived in the Bayesian framework. The spatial adaptivity allows the additional information of the image (such as identification of homogeneous or heterogeneous regions) to be incorporated into the algorithm. Further, the threshold has been extended to the redundant wavelet representation, which yields better results than the decimated wavelet transform. Experimental results demonstrate the improved performance of the proposed method over other well-known speckle reduction filters. The application of the proposed method to a realistic US test image shows that the new technique, named HomoGenThresh, outperforms the best wavelet-based denoising method reported in [1] by more than 1.6 dB, Lee filter by 3.6 dB, Kaun filter by 3.1 dB and band-adaptive soft thresholding [2] by 2.1 dB at an input signal-to-noise ratio (SNR) of 13.6 dB.

Algorithms↗
PubMed2002

Filtering point spread function in backprojection cone-beam CT and its applications in long object imaging.

In backprojection cone-beam CT the cone-beam projection images are first filtered, then 3D backprojected into the object space. In this paper the point spread function (PSF) for the filtering operation is studied. For the cases where the normalization matrix is a constant, i.e. all integration planes intersect the scan path the same number of times, the derivation of the PSF is extended to the general case of limited angular range for the Radon line integrals. It is found that the 2D component of the PSF can be reduced to the form of space-variant 1D Hilbert transforms. The application of the PSF to a number of aspects in long object imaging will be discussed.

Algorithms↗
PubMed2002

Implementation of continuous 3D whole-body PET scanning using on-the-fly Fourier rebinning.

The continuous scanning mode in three-dimensional (3D) whole-body PET studies has the advantage of axial sensitivity uniformity over the majority of the axial FOV. However, this scan mode requires large data handling compared to conventional discrete scans. In this work, we have implemented and evaluated a new continuous 3D scan method using 'on-the-fly' Fourier rebinning. In this method, sinograms for the pair of rings are added in real-time into the sinograms of the incremented ring pairs by moving the bed axially one detector width at a time. For an N-ring scanner, 2N--1 sinograms are transferred to a host computer at each bed position and rebinned into direct two-dimensional (2D) sinograms using Fourier rebinning. Phantom and human studies showed that the axial image uniformity is achieved without degrading the axial resolution. This method can minimize the time for off-line data processing and makes the continuous 3D scan more feasible in clinical whole-body studies.

Fourier Analysis↗
PubMed2002

Measurements of MTF and SNR(f) using a subtraction method in MRI.

A method was developed for accurate measurement of the modulation transfer function (MTF) and signal-to-noise ratio in the spatial frequency domain (SNR(f)) of magnetic resonance images (MRI). The MTF was calculated from the complex images of a line object which were obtained by the subtraction of two separately acquired data sets of a specially designed phantom with a sliding sheet. Moreover, the SNR(f) was calculated from the MTF and Wiener spectrum, both of which were determined using the same phantom configuration. The MTFs and SNR(f)s in the conventional spin-echo (SE) and turbo SE, in which the effective echo time was set to the first echo, were evaluated by changing the T2 of the phantom and the echo train length. The MTFs in the positive and negative frequencies indicated the effect of the k-space trajectory for each pulse sequence. SNR(f)s gave spatial frequency information that was not obtained with conventional methods. In this method, the influence of image nonuniformity and unwanted artefacts (edge and ghost) could be eliminated. An analysis of the MTF and the SNR in the spatial frequency domain provides additional information for the assessment of image quality in MRI.

Artifacts↗
PubMed2002

On the de-noising of Monte Carlo calculated dose distributions.

This paper presents an algorithm for de-noising Monte Carlo calculated dose distributions for use in radiation treatment planning. The algorithm is a three-dimensional generalization of a Savitzky-Golay digital filter and uses an adaptive smoothing window size to reduce the probability for systematic bias. The paper also introduces five accuracy criteria that are relevant for the expected clinical use of Monte Carlo techniques, which can be used to evaluate the performance of smoothing algorithms. Using these accuracy criteria it is demonstrated that the smoothing algorithm presented here decreases the uncertainty of Monte Carlo calculated dose distributions. The corresponding decrease in necessary particle tracks ranges from a factor of 2 to a factor of 20, depending on the accuracy criterion used. It is shown that very short Monte Carlo simulations combined with smoothing deliver satisfactory dose distributions and may therefore be extremely valuable for the initial trial and error phase of the radiation treatment planning process.

Air↗
PubMed2002

Anisotropic diffusion filtering of PET attenuation data to improve emission images.

Attenuation correction in positron emission tomography (PET) is an essential part of clinical and research studies. However, correction using noisy transmission data acquired over short scan durations has been a problem as the noise is introduced into emission images. This study investigates the effect of smoothing the two-dimensional projections of the attenuation maps (mu-map) using the nonlinear anisotropic diffusion filtering method. Experiments are presented on a whole-body study to qualitatively evaluate the efficacy of the method in reducing the random noise and streak artefacts. The results show that image quality is significantly improved with minimal resolution loss. A reduction in statistical noise was quantitatively demonstrated when the same approach was applied to a cylindrical phantom dataset.

Algorithms↗
PubMed2003

Adaptive anisotropic diffusion filtering of Monte Carlo dose distributions.

The Monte Carlo method is the most accurate method for radiotherapy dose calculations, if used correctly. However, any Monte Carlo dose calculation is burdened with statistical noise. In this paper, denoising of Monte Carlo dose distributions with a three-dimensional adaptive anisotropic diffusion method was investigated. The standard anisotropic diffusion method was extended by changing the filtering parameters adaptively according to the local statistical noise. Smoothing of dose distributions with different noise levels in an inhomogeneous phantom, a conventional and an IMRT treatment case is shown. The resultant dose distributions were analysed using several evaluating criteria. It is shown that the adaptive anisotropic diffusion method can reduce statistical noise significantly (two to five times, corresponding to the reduction of simulation time by a factor of up to 20), while preserving important gradients of the dose distribution well. The choice of free parameters of the method was found to be fairly robust.

Anisotropy↗
PubMed2003

A wavelet-based spatially adaptive method for mammographic contrast enhancement.

A method aimed at minimizing image noise while optimizing contrast of image features is presented. The method is generic and it is based on local modification of multiscale gradient magnitude values provided by the redundant dyadic wavelet transform. Denoising is accomplished by a spatially adaptive thresholding strategy, taking into account local signal and noise standard deviation. Noise standard deviation is estimated from the background of the mammogram. Contrast enhancement is accomplished by applying a local linear mapping operator on denoised wavelet magnitude values. The operator normalizes local gradient magnitude maxima to the global maximum of the first scale magnitude subimage. Coefficient mapping is controlled by a local gain limit parameter. The processed image is derived by reconstruction from the modified wavelet coefficients. The method is demonstrated with a simulated image with added Gaussian noise, while an initial quantitative performance evaluation using 22 images from the DDSM database was performed. Enhancement was applied globally to each mammogram, using the same local gain limit value. Quantitative contrast and noise metrics were used to evaluate the quality of processed image regions containing verified lesions. Results suggest that the method offers significantly improved performance over conventional and previously reported global wavelet contrast enhancement methods. The average contrast improvement, noise amplification and contrast-to-noise ratio improvement indices were measured as 9.04, 4.86 and 3.04, respectively. In addition, in a pilot preference study, the proposed method demonstrated the highest ranking, among the methods compared. The method was implemented in C++ and integrated into a medical image visualization tool.

Algorithms↗
PubMed2004

A new weighting scheme for cone-beam helical CT to reduce the image noise.

Reducing the patient dose while keeping the image noise at the same level is desired for x-ray CT examinations. In order to achieve the goal, we propose a new weighting scheme taking the validity of the data and redundant data samples into account. The method is evaluated with a new generalized version of the Feldkamp helical reconstruction algorithm. It allows us to enlarge the projection angular range used in reconstruction, and thus, to reduce the image noise by increasing the detector utilization rate to 100% without sacrificing the image quality or z-resolution. This concept can be adapted to other exact or approximate algorithms as far as they use redundant data samples.

Algorithms↗
PubMed2004

Physiological noise in murine solid tumours using T2*-weighted gradient-echo imaging: a marker of tumour acute hypoxia?

T2*-weighted gradient-echo magnetic resonance imaging (T2*-weighted GRE MRI) was used to investigate spontaneous fluctuations in tumour vasculature non-invasively. FSa fibrosarcomas, implanted intramuscularly (i.m.) in the legs of mice, were imaged at 4.7 T, over a 30 min or 1 h sampling period. On a voxel-by-voxel basis, time courses of signal intensity were analysed using a power spectrum density (PSD) analysis to isolate voxels for which signal changes did not originate from Gaussian white noise or linear drift. Under baseline conditions, the tumours exhibited spontaneous signal fluctuations showing spatial and temporal heterogeneity over the tumour. Statistically significant fluctuations occurred at frequencies ranging from 1 cycle/3 min to 1 cycle/h. The fluctuations were independent of the scanner instabilities. Two categories of signal fluctuations were reported: (i) true fluctuations (TFV), i.e., sequential signal increase and decrease, and (ii) profound drop in signal intensity with no apparent signal recovery (SDV). No temporal correlation between tumour and contralateral muscle fluctuations was observed. Furthermore, treatments aimed at decreasing perfusion-limited hypoxia, such as carbogen combined with nicotinamide and flunarizine, decreased the incidence of tumour T2*-weighted GRE fluctuations. We also tracked dynamic changes in T2* using multiple GRE imaging. Fluctuations of T2* were observed; however, fluctuation maps using PSD analysis could not be generated reliably. An echo-time dependency of the signal fluctuations was observed, which is typical to physiological noise. Finally, at the end of T2*-weighted GRE MRI acquisition, a dynamic contrast-enhanced MRI was performed to characterize the microenvironment in which tumour signal fluctuations occurred in terms of vessel functionality, vascularity and microvascular permeability. Our data showed that TFV were predominantly located in regions with functional vessels, whereas SDV occurred in regions with no contrast enhancement as the result of vessel functional impairment. Furthermore, transient fluctuations appeared to occur preferentially in neoangiogenic hyperpermeable vessels. The present study suggests that spontaneous T2*-weighted GRE fluctuations are very likely to be related to the spontaneous fluctuations in blood flow and oxygenation associated with the pathophysiology of acute hypoxia in tumours. The disadvantage of the T2*-weighted GRE MRI technique is the complexity of signal interpretation with regard to pO2 changes. Compared to established techniques such as intravital microscopy or histological assessments, the major advantage of the MRI technique lies in its capacity to provide simultaneously both temporal and detailed spatial information on spontaneous fluctuations throughout the tumour.

Animals↗