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Results for “Deconvolution”

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PubMed2000

Three-dimensional blind deconvolution of SPECT images.

Thanks to its ability to yield functionally rather than anatomically-based information, the three-dimensional (3-D) SPECT imagery technique has become a great help in the diagnostic of cerebrovascular diseases. Nevertheless, due to the imaging process, the 3-D single photon emission computed tomography (SPECT) images are very blurred and, consequently, their interpretation by the clinician is often difficult and subjective. In order to improve the resolution of these 3-D images and then to facilitate their interpretation, we propose herein to extend a recent image blind deconvolution technique (called the nonnegativity support constraint-recursive inverse filtering deconvolution method) in order to improve both the spatial and the interslice resolution of SPECT volumes. This technique requires a preliminary step in order to find the support of the object to be restored. In this paper, we propose to solve this problem with an unsupervised 3-D Markovian segmentation technique. This method has been successfully tested on numerous real and simulated brain SPECT volumes, yielding very promising restoration results.

Algorithms↗

PubMed2001

Deconvolution and wavelet-based methods for membrane current estimation from simulated fractionated electrograms.

In infarcted myocardium, extracellular recordings exhibit multiple deflections due to irregular pathway of the electric impulse. In this work the problem of distinguishing local from distant deflections is tackled. In order to evaluate the proposed methods in a controlled setting, simulated data are used, following both Beeler-Reuter and Luo-Rudy kinetics. The input is an array of electrograms positioned on grid-points of a rectangular grid and the output is an array of estimates of the membrane current. First, deconvolution techniques are used in the form of spatial filtering for membrane current estimation from the extracellular recordings. Second, the extracellular recordings undergo wavelet based transformation, followed by a spatial filter which enhances local activity deflections and suppresses distant activity deflections. It is shown that wavelet filtering of the extracellular recordings acts as an evaluator of the efficiency of the deconvolution techniques for the membrane current estimation. Subsequently, activation times based on the results from the two methods are used for the reconstruction of the propagation pattern in a zig-zag case in two-dimensional grids. It is shown that the wavelet-based method is more robust, and can work well even in cases where the grid interval in the y direction is four times larger than the single cell size.

Algorithms↗

PubMed2001

Reconstructing insulin secretion rate after a glucose stimulus by an improved stochastic deconvolution method.

Reconstructing insulin secretion rate (ISR) after a glucose stimulus by deconvolution is difficult because of its biphasic pattern, i.e., a rapid secretion peak is followed by a slower release. Here, we refine a recently proposed stochastic deconvolution method by modeling ISR as the multiple integration of a white noise process with time-varying statistics. The unknown parameters are estimated from the data by employing a maximum likelihood criterion. A fast computational scheme implementing the method is presented. Monte Carlo simulation results are developed which numerically show a more reliable ISR profile reconstructed by the new method.

Biomedical Engineering↗

PubMed2001

Two-dimensional noise-robust blind deconvolution of ultrasound images.

This paper presents a new method for 2-D blind homomorphic deconvolution of medical B-scan ultrasound images. The method is based on noise-robust 2-D phase unwrapping and a noise-robust procedure to estimate the pulse in the complex cepstrum domain. Ordinary Wiener filtering is used in the subsequent deconvolution. The resulting images became much sharper with better defined tissue structures compared with the ordinary images. The deconvolved images had a resolution gain along the order of 3 to 7, and the signal-to-noise ratio (SNR) doubled for many of the images used in our experiments. The method gave stable results with respect to noise and grey levels through several image sequences.

Adult↗

PubMed2005

A soft double regularization approach to parametric blind image deconvolution.

This paper proposes a blind image deconvolution scheme based on soft integration of parametric blur structures. Conventional blind image deconvolution methods encounter a difficult dilemma of either imposing stringent and inflexible preconditions on the problem formulation or experiencing poor restoration results due to lack of information. This paper attempts to address this issue by assessing the relevance of parametric blur information, and incorporating the knowledge into the parametric double regularization (PDR) scheme. The PDR method assumes that the actual blur satisfies up to a certain degree of parametric structure, as there are many well-known parametric blurs in practical applications. Further, it can be tailored flexibly to include other blur types if some prior parametric knowledge of the blur is available. A manifold soft parametric modeling technique is proposed to generate the blur manifolds, and estimate the fuzzy blur structure. The PDR scheme involves the development of the meaningful cost function, the estimation of blur support and structure, and the optimization of the cost function. Experimental results show that it is effective in restoring degraded images under different environments.

Algorithms↗

PubMed2005

Deconvolution methods for mitigation of transverse blurring in optical coherence tomography.

Imaging resolution in optical coherence tomography (OCT) is a key determinant for acquiring clinically useful optical biopsies of tissues. In contrast to light or confocal microscopy, the axial and transverse resolutions in OCT are independent and each can be analyzed individually. A method for mitigating transverse blurring and the apparent loss of transverse resolution in OCT by means of Gaussian beam deconvolution is presented. Such a method provides better representation of a specimen by using known physical parameters of a lens. To implement this method, deconvolution algorithms based on a focal-dependent kernel are investigated. First, the direct inverse problem is investigated using two types of regularization, truncated singular value decomposition, and Tikhonov. Second, an iterative expectation maximization algorithm, the Richardson-Lucy algorithm, with a beam-width-dependent iteration scheme is developed. A dynamically iterative Richardson-Lucy algorithm can reduce transverse blurring by providing an improvement in the transverse point-spread-function for sparse scattering samples in regions up to two times larger than the confocal region of the lens. These deblurring improvements inside and outside of the confocal region, which are validated experimentally, are possible without introducing new optical imaging hardware or acquiring multiple images of the same specimen. Implementation of this method in sparse scattering specimens, such as engineered tissues, has the potential to improve cellular detection and categorization.

Algorithms↗

PubMed2006

A segmentation-based regularization term for image deconvolution.

This paper proposes a new and original inhomogeneous restoration (deconvolution) model under the Bayesian framework for observed images degraded by space-invariant blur and additive Gaussian noise. In this model, regularization is achieved during the iterative restoration process with a segmentation-based a priori term. This adaptive edge-preserving regularization term applies a local smoothness constraint to pre-estimated constant-valued regions of the target image. These constant-valued regions (the segmentation map) of the target image are obtained from a preliminary Wiener deconvolution estimate. In order to estimate reliable segmentation maps, we have also adopted a Bayesian Markovian framework in which the regularized segmentations are estimated in the maximum a posteriori (MAP) sense with the joint use of local Potts prior and appropriate Gaussian conditional luminance distributions. In order to make these segmentations unsupervised, these likelihood distributions are estimated in the maximum likelihood sense. To compute the MAP estimate associated to the restoration, we use a simple steepest descent procedure resulting in an efficient iterative process converging to a globally optimal restoration. The experiments reported in this paper demonstrate that the discussed method performs competitively and sometimes better than the best existing state-of-the-art methods in benchmark tests.

Algorithms↗

PubMed2004

Fast fixed-point neural blind-deconvolution algorithm.

The aim of this letter is to introduce a new blind-deconvolution algorithm based on fixed-point optimization of a "Bussgang"-type cost function. The cost function relies on approximate Bayesian estimation achieved by an adaptive neuron. The main feature of the presented algorithm is fast convergence that guarantees good deconvolution performances with limited computational demand as compared with algorithms of the same class.

Algorithms↗

PubMed2004

Multisensor optimal information fusion input white noise deconvolution estimators.

The unified multisensor optimal information fusion criterion weighted by matrices is rederived in the linear minimum variance sense, where the assumption of normal distribution is avoided. Based on this fusion criterion, the optimal information fusion input white noise deconvolution estimators are presented for discrete time-varying linear stochastic control system with multiple sensors and correlated noises, which can be applied to seismic data processing in oil exploration. A three-layer fusion structure with fault tolerant property and reliability is given. The first fusion layer and the second fusion layer both have netted parallel structures to determine the first-step prediction error cross-covariance for the state and the estimation error cross-covariance for the input white noise between any two sensors at each time step, respectively. The third fusion layer is the fusion center to determine the optimal matrix weights and obtain the optimal fusion input white noise estimators. The simulation results for Bernoulli-Gaussian input white noise deconvolution estimators show the effectiveness.

Algorithms↗

PubMed2003

Bayesian deconvolution analysis of pulsatile hormone concentration profiles.

Many hormones are secreted into the circulatory system in a pulsatile manner and are cleared exponentially. The most common method of analyzing these systems is to deconvolve the hormone concentration into a secretion function and a clearance function. Accurate estimation of the model parameters depends on the number and location of the secretion pulses. To date, deconvolution analysis assumes the number and approximate location of these pulses are known a priori. In this article, we present a novel Bayesian approach to deconvolution that jointly models the number of pulses along with all other model parameters. Our method stochastically searches for the secretion pulses. This is accomplished by viewing the set of parameters that define the pulses as a point process. Pulses are determined by a birth-death process which is embedded in Markov chain Monte Carlo algorithm. This idea originated with Stephens (2000, Annals of Statistics 28, 40-74) in the context of finite mixture model density estimation, where the number of mixture components is unknown. There are several advantages that our model enjoys over the traditional frequentist approaches. These advantages are highlighted with four datasets consisting of serum concentration levels of luteinizing hormone obtained from ovariectomized ewes.

Animals↗

PubMed2001

Estimation and prediction for cancer screening models using deconvolution and smoothing.

The model that specifies that cancer incidence, I, is the convolution of the preclinical incidence, g, and the density of time in the preclinical phase, f, has frequently been utilized to model data from cancer screening trials and to estimate such quantities as sojourn time, lead time, and sensitivity. When this model is fit to the above data, the parameters of f as well as the parameter(s) governing screening sensitivity must be estimated. Previously, g was either assumed to be equal to clinical incidence or assumed to be a constant or exponential function that also had to be estimated. Here we assume that the underlying incidence, I, in the study population (in the absence of screening) is known. With I known, g then becomes a function of f, which can be solved for using (numerical) deconvolution, thus eliminating the need to estimate g or make assumptions about it. Since numerical deconvolution procedures may be highly unstable, however, we incorporate a smoothing procedure that produces a realistic g function while still closely reproducing the original incidence function I upon convolution with f. We have also added the concept of competing mortality to the convolution model. This, along with the realistic preclinical incidence function described above, results in more accurate estimates of sojourn time and lead time and allows for estimation of quantities related to overdiagnosis, which we define here.

Clinical Trials as Topic↗

PubMed2005

Noise suppression of point spread functions and its influence on deconvolution of three-dimensional fluorescence microscopy image sets.

The point spread function (PSF) is of central importance in the image restoration of three-dimensional image sets acquired by an epifluorescent microscope. Even though it is well known that an experimental PSF is typically more accurate than a theoretical one, the noise content of the experimental PSF is often an obstacle to its use in deconvolution algorithms. In this paper we apply a recently introduced noise suppression method to achieve an effective noise reduction in experimental PSFs. We show with both simulated and experimental three-dimensional image sets that a PSF that is smoothed with this method leads to a significant improvement in the performance of deconvolution algorithms, such as the regularized least-squares algorithm and the accelerated Richardson-Lucy algorithm.

Algorithms↗

PubMed2006

Clear imaging of Förster resonance energy transfer (FRET) signals of Ras activation by a time-lapse three-dimensional deconvolution system.

A procedure for a time series of three-dimensional Förster resonance energy transfer observation of a cell has been established. It employs quantitative deconvolution and three-dimensional views of intensity-modulated displays. The development was done with Raichu, a synthetic Ras protein capable of producing Förster resonance energy transfer upon its activation, for which two-dimensional imaging has been established. This method gave much clearer images of Förster resonance energy transfer than does the usual method without deconvolution, even though the signals were relatively weak. The results suggest that this procedure is compatible with weak fluorescent light, which is prone to photobleaching.

Animals↗

PubMed1992

Mössbauer characterization of the tetraheme cytochrome c3 from Desulfovibrio baculatus (DSM 1743). Spectral deconvolution of the heme components.

Mössbauer spectroscopy was used to study the tetraheme cytochrome c3 from Desulfovibrio baculatus (DSM 1743). Samples with different degrees of reduction were prepared using a redoxtitration technique. In the reduced cytochrome c3, all four hemes are reduced and exhibit diamagnetic Mössbauer spectra typical for low-spin ferrous hemes (S = 0). In the oxidized protein, the hemes are low-spin ferric (S = 1/2) and exhibit overlapping magnetic Mössbauer spectra. A method of differential spectroscopy was applied to deconvolute the four overlapping heme spectra and a crystal-field model was used for data analysis. Characteristic Mössbauer spectral components for each heme group are obtained. Hyperfine and crystal-field parameters for all four hemes are determined from these deconvoluted spectra.

Cytochrome c Group↗

PubMed1999

Estimation of frequently sampled nocturnal melatonin production in humans by deconvolution analysis: evidence for episodic or ultradian secretion.

In order to investigate nocturnal melatonin production and clearance rates, we applied deconvolution analysis to plasma melatonin concentration time series obtained every 20 min for 12 hr from 20:00 h to 08:00 h in two groups of healthy subjects at rest (group 1, 12 male subjects, 22-26 yr; group 2, ten female subjects, 31-42 yr). The estimated melatonin production rate from group 1 (0.55 +/- 0.21 microg/kg/night) was significantly higher than that of group 2 (0.26 +/- 0.19 microg/kg/night), as well as the mass of melatonin released per burst (275 +/- 110 vs. 145 +/- 130 pg/mL for groups 1 and 2, respectively), the amplitude of secretory bursts (7.8 +/- 3.2 vs. 4.7 +/- 3.5 pg/mL/min), and the pulsatile melatonin production rate (2.76 +/- 1.14 vs. 1.27 +/- 0.97 ng/mL/night). These differences could reflect alterations related to age and or gender. No differences were observed between the two groups in the secretory burst half-duration and frequency and the interburst interval. Melatonin production rates, as estimated by deconvolution analysis, are in agreement with other independent estimates, especially the isotopic method, and disclose an ultradian rhythmicity.

Adult↗

PubMed2002

Myocardial blood flow quantification with MRI by model-independent deconvolution.

Magnetic resonance (MR) imaging during the first pass of an injected contrast agent has been used to assess myocardial perfusion, but the quantification of blood flow has been generally judged as too complex for its clinical application. This study demonstrates the feasibility of applying model-independent deconvolution to the measured tissue residue curves to quantify myocardial perfusion. Model-independent approaches only require minimal user interaction or expertise in modeling. Monte Carlo simulations were performed with contrast-to-noise ratios typical of MR myocardial perfusion studies to determine the accuracy of the resulting blood flow estimates. With a B-spline representation of the tissue impulse response and Tikhonov regularization, the bias of blood flow estimates obtained by model-independent deconvolution was less than 1% in all cases for peak contrast to noise ratios in the range from 15:1 to 20:1. The relative dispersion of blood flow estimates in Monte Carlo simulations was less than 7%. Comparison of MR blood flow estimates against measurements with radio-isotope labeled microspheres indicated excellent linear correlation (R2 = 0.995, slope: 0.96, intercept: 0.06). It can be concluded from these studies that the application of myocardial blood flow quantification with MRI can be performed with model-independent methods, and this should support a more widespread use of blood flow quantification in the clinical environment.

Animals↗

PubMed2006

Deblurring of breathing motion artifacts in thoracic PET images by deconvolution methods.

In FDG-PET imaging of thoracic tumors, blurring due to breathing motion often significantly degrades the quality of the observed image, which then obscures the tumor boundary. We demonstrate a deblurring technique that combines patient-specific motion estimates of tissue trajectories with image deconvolution techniques, thereby partially eliminating breathing-motion induced artifacts. Two data sets were used to evaluate the methodology including mobile phantoms and clinical images. The clinical images consist of PET/CT co-registered images of patients diagnosed with lung cancer. A breathing motion model was used to locally estimate the location-dependent tissue location probability function (TLP) due to breathing. The deconvolution process is carried by an expectation-maximization (EM) iterative algorithm using the motion-based TLP. Several methods were used to improve the robustness of the deblurring process by mitigating noise amplification and compensating for motion estimate uncertainties. The mobile phantom study with controlled settings demonstrated significant reduction in underestimation error of concentration in high activity case without significant superiority between the different applied methods. In case of medium activity concentration (moderate noise levels), less improvement was reported (10%-15% reduction in underestimation error relative to 15%-20% reduction in high concentration). Residual denoising using wavelets offered the best performance for this case. In the clinical data case, the image spatial resolution was significantly improved, especially in the direction of greatest motion (cranio-caudal). The EM algorithm converged within 15 and 5 iterations in the large and small tumor cases, respectively. A compromise between a figure-of-merit and entropy minimization was suggested as a stopping criterion. Regularization techniques such as wavelets and Bayesian methods provided further refinement by suppressing noise amplification. Our initial results show that the proposed method provides a feasible framework for improving PET thoracic images, without the need for gated/4-D PET imaging, when 4-D CT is available to estimate tumor motion.

Algorithms↗

PubMed2005

A method for deconvolution of integrated electronic portal images to obtain incident fluence for dose reconstruction.

A method to convert integrated electronic portal imaging device (EPID) images to fluence for the purpose of reconstructing the dose to a phantom is investigated here for simple open fields. Ultimately, the goal is to develop a method to reconstruct the dose to patients. The EPID images are transformed into incident intensity fluence by spatial filtering with a deconvolution kernel. The kernel uses a general mathematical form derived from a Monte Carlo calculation of the point spread function of an EPID. The deconvolution kernel is fitted using a downhill search algorithm that minimizes the difference between the reconstructed dose and the dose measured in water. The beam profile "horns" that are removed by the EPID calibration procedure are restored to the resulting images by direct multiplication using the measured in-air off-axis ratio. Applying the fitted kernel to an EPID image provides the incident fluence for that beam. This beam fluence is then entered into an independent dose calculation algorithm for phantom or patient dose reconstruction. The phantom dose was computed to an accuracy of 2.0% of the dmax dose at one standard deviation. The method is general and can possibly be applied to any EPID equipped with an integration mode. We demonstrate the application of the fitted kernel in two clinical IMRT cases.

Algorithms↗