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PubMed1999

Optimizing deconvolution techniques by the application of the Münchhausen meta algorithm.

A deconvolution applied to disturbed data often gives poor results, due to fundamental difficulties associated with ill-posed problems. Many numerical and theoretical methods have been invented to circumvent this phenomenon. Their performance varies, depending on the given problem and data. The main aim of this paper is to provide a decision rule for choosing a method for deconvolution and application of this method to the same data. We have called this meta-algorithm Münchhausen. In this paper we introduce and describe for the first time the basic principle of artificial disturbance of the data in the set-up of deconvolution. We demonstrate some interesting features of the random procedure Münchhausen, such as the non parametric set-up, robustness to disturbance of the data and last but not least good performance.

Algorithms↗
PubMed2005

Estimation of in vivo percutaneous absorption of emedastine from bile excretion data using a deconvolution method.

In vivo percutaneous absorption of emedastine difumarate was investigated in rats and compared with rat skin in vitro. Since emedastine entering the systemic circulation is mostly excreted in bile, we first came up with the method of collecting bile with a minimal skin incision. In vivo skin permeation of the drug was estimated from biliary excretion data by deconvolution analysis. Prior to applying deconvolution analysis, it was confirmed that biliary excretion of emedastine was linear against its dose. When the in vivo permeation profile estimated by deconvolution was compared with the in vitro profile, the lag time for permeation was significantly shorter in vivo than in vitro, whereas the skin permeability coefficient was almost the same. If we presume a two-layer diffusion model, then this finding may primarily be due to the shorter diffusion length of the dermis.

Administration, Cutaneous↗
PubMed2001

A workingperson's guide to deconvolution in light microscopy.

Thefluorescence microscope is routinely used to study cellular structure in many biomedical research laboratories and is increasingly used as a quantitative assay system for cellular dynamics. One of the major causes of image degradation in the fluorescence microscope is blurring. Deconvolution algorithms use a model of the microscope imaging process to either subtract or reassign out-of-focus blur. A variety of algorithms are now commercially available, each with its own characteristic advantages and disadvantages. In this article, we review the imaging process in the fluorescence microscope and then discuss how the various deconvolution methods work. Finally, we provide a summary of practical tips for using deconvolution and discuss imaging artifacts and how to minimize them.

Algorithms↗
PubMed2003

[Use of three-dimensional fluorescence deconvolution microscopy for study of spatial distribution of secretory vesicles in living cells].

A three-dimensional image of a living cell is helpful for cell secretion study. In this report, the three-dimensional fluorescence deconvolution microscopy for observing living cells was studied, because this technique can obtain a quick three-dimensional imaging with minimal fluorescence quenching and cytotoxicity for living cell observation. The property of three-dimensional point spread function (PSF) of imaging system was analyzed. The relationship between experimental and theoretical PSF was illustrated, and the theoretical PSF was proved that it could reflect the principle of imaging system with NA 1.65 objective in use. Three-dimensional deconvolution algorithm in this report was proved effective by well-defined three-dimensional specimens. Furthermore, the rat pancreatic beta cell secretory vesicles labeled by acridine orange was observed by using this technique. Results showed that the blurring induced by out-of-focus light was removed by the deconvolution algorithm effectively, under current experiment conditions (with NA 1.65 objective) the experimental PSF approximated the theoretical PSF very well, and deconvolved living cell images exhibited the spatial distribution of the secretory vesicles clearly.

Algorithms↗
PubMed2004

[A comparative study on the resolution of second derivative and Fourier self-deconvolution].

Infrared spectra of 2-alkyl-7,7,8,8-tetracyanoquinodimethane (C12H25 TCNQ, C15H31 TCNQ, C18H37 TCNQ) were measured with the resolution of 1 and 4 cm(-1). In order to identify the peak number correctly in the CH2 stretching region, second derivative and Fourier self-deconvolution were applied to the infrared spectra, respectively. The overlapping bands in the CH2 stretching region could be identified when the infrared spectra, which were measured with the resolution of 4 cm(-1), were dealt with by Fourier self-deconvolution. However, the bands overlapped in the CH2 symmetric stretching region could not be observed when these infrared spectra were dealt with by second derivative. The above results reveal that Fourier self-deconvolution method is more powerful than second derivative in identifying bands that are involved in an overlapping band feature.

Models, Chemical↗
PubMed1990

Deconvolution study of the absorption rate and disposition kinetic values of lindane in sheep.

Absorption rate and plasma and fat disposition of lindane after various lindane percutaneous treatments in shorn and unshorn sheep were investigated. To analyze data with a deconvolution method, IV administration was performed to determine the basic pharmacokinetic values of lindane in sheep. After IV administration, the steady state volume of distribution was very high (8.07 +/- 3.60 L/kg of body weight), and the mean residence time was long (28.1 +/- 11.7 hours). Deconvolution analysis indicated that lindane absorption was continuous until 33 to 41 days after spraying with a 0.025% lindane solution. Total amount of absorbed lindane in shorn (15,171 +/- 4,463 micrograms/kg) sheep was about twice that in unshorn (7,615 +/- 3,128 micrograms/kg) sheep; from deconvolution analysis, it was calculated that the time required for 50% of the available dose to be absorbed was between 115 and 179 hours. After percutaneous lindane administration, the fat concentration was compared with the available lindane dose. The apparent half-life of lindane elimination in fat was 225 +/- 47.4 hours, which is similar to the value calculated for the absorption rate constant. By comparing fat and plasma concentrations, it was calculated that for a mean plasma concentration of 5 ng/ml, the fat lindane concentration was 1.65 +/- 0.87 micrograms/g (ie, lower than the generally accepted tolerance level of 2 micrograms/g).

Adipose Tissue↗
PubMed1990

An overview of computer algorithms for deconvolution-based assessment of in vivo neuroendocrine secretory events.

The availability of increasingly efficient computational systems has made feasible the otherwise burdensome analysis of complex neurobiological data, such as in vivo neuroendocrine glandular secretory activity. Neuroendocrine data sets are typically sparse, noisy and generated by combined processes (such as secretion and metabolic clearance) operating simultaneously over both short and long time spans. The concept of a convolution integral to describe the impact of two or more processes acting jointly has offered an informative mathematical construct with which to dissect (deconvolve) specific quantitative features of in vivo neuroendocrine phenomena. Appropriate computer-based deconvolution algorithms are capable of solving families of 100-300 simultaneous integral equations for a large number of secretion and/or clearance parameters of interest. For example, one application of computer technology allows investigators to deconvolve the number, amplitude and duration of statistically significant underlying secretory episodes of algebraically specifiable waveform and simultaneously estimate subject- and condition-specific neurohormone metabolic clearance rates using all observed data and their experimental variances considered simultaneously. Here, we will provide a definition of selected deconvolution techniques, review their conceptual basis, illustrate their applicability to biological data and discuss new perspectives in the arena of computer-based deconvolution methodologies for evaluating complex biological events.

Algorithms↗
PubMed1985

Deconvolution of Compton scatter in SPECT.

A deconvolution algorithm has been developed which compensates for Compton scattering in SPECT images. Compton scatter is modeled as a convolution of the nonscattered projection data with an exponential function. Deconvolution of the total (scatter + nonscatter) projection data yields compensated true projection. Using Monte Carlo methods, the scattered and nonscattered components of a SPECT image are simulated thus allowing a comparison of scatter compensated results with direct nonscatter results. The quality of the compensation is evaluated by comparing the ratio of total to direct counts with the ratio of compensated to direct counts. This deconvolution technique has been developed and evaluated for experimentally acquired SPECT data as well as for simulated data.

Filtration↗
PubMed1996

Reconstruction of insulin secretion rate by deconvolution: domain of validity of a monoexponential C-peptide impulse response model.

Insulin secretion rate (ISR) in vivo is reconstructed by deconvolution from plasma concentration of C-peptide (CP), a peptide with linear kinetics which is co-secreted with insulin but is not extracted by the liver. Deconvolution requires the knowledge of the CP impulse response. A two-exponential (2E) model is usually chosen to describe the CP impulse response but a one-exponential (1E) model is also used in the literature. The purpose here is to discuss the domain of validity of the 1E model in reconstructing the ISR by deconvolution. In particular, we show that the 1E model can be reliably used only if the ISR spectrum is concentrated in a narrow frequency band and a suitable input is designed for its identification.

Activity Cycles↗
PubMed2025

Continuous DNA Methylation Deconvolution-Based Surrogate for B-Cell Differentiation State in CLL.

Chronic Lymphocytic Leukemia (CLL) is clinically divided into IGHV mutated (M-CLL) and IGHV unmutated (U-CLL) subtypes, which are thought to arise from distinct cells of origin along the B-cell differentiation pathway. We measured genome-scale DNA methylation in purified CLL samples ( n = 89) and utilized reference-based cell deconvolution techniques to develop a continuous metric of epigenetic similarity across a B-naive-like to B-memory-like scale (B-Index). B-Index accurately classifies CLL into clinical subtypes (98.8%), has a stronger epigenetic signal than IGHV gene percent identity, and demonstrates additional epigenetic signal within the M-CLL subgroup. We demonstrate that U-CLL is epigenetically more similar to B-memory than B-naive cells and reconcile previous reports of a B-naive-like epigenetic signal. The B-memory-like program of U-CLL is enriched for binding sites of transcription factors related to the germinal center activation pathway. Our findings provide epigenetic evidence for discerning CLL mechanisms of initiation and cell of origin. We also identified an epigenetic signal associated with tumor burden, which may have some relation to viral infections such as Epstein-Barr-Virus. Our cell-type deconvolution-based approach to developing a continuous metric for CLL epigenetic differentiation state can be applied to other tumors with multiple subtypes across differentiation stages.

B-memory-like↗
PubMed1999

Deconvolution gas chromatography/mass spectrometry of urinary organic acids--potential for pattern recognition and automated identification of metabolic disorders.

The National Institute of Standards and Technology (NIST) Automated Mass Spectral Deconvolution and Identification System (AMDIS) is applied to a selection of data files obtained from the gas chromatography/mass spectrometry (GC/MS) analysis of urinary organic acids. Mass spectra obtained after deconvolution are compared with a special user library containing both the mass spectra and retention indices of ethoxime-trimethylsilyl (EO-TMS) derivatives of a set of organic acids. Efficient identification of components is achieved and the potential of the procedure for automated diagnosis of inborn errors of metabolism and for related research is demonstrated.

Acyl-CoA Dehydrogenase↗
PubMed1996

Determination of the secondary structure of isomeric forms of human serum albumin by a particular frequency deconvolution procedure applied to Fourier transform IR analysis.

A new deconvolution procedure was applied to the analysis of Fourier transform ir spectra of human serum albumin secondary structure in the native state and in states denatured by heat and acid treatment. The deconvolution method is based on the use of the Conjugate Gradient Minimization Algorithm, with the addition of suitable constraints directly obtained by the application to the measured spectrum of the second derivative operator. This method computes central band frequency, bandwidth, and amplitude of the different spectral components of conformation-sensitive amide bands. In the specific case, it was applied to analysis of the amide I band, and the quantitative determination of the different secondary structures (alpha-helix, beta-sheet, beta-turns, and random) was attempted for all the samples examined. The precision of the quantitative determination depends on the amounts of these structures present in the protein. The coefficient of variation is < 10% for values of amide I component > 15%. The accuracy was tested by comparing, by means of linear regression, the results obtained for human serum albumin, hemoglobin, alpha-chymotrypsin, and cytochrome c, using our method, with those obtained by x-ray crystallography and CD; the results obtained by other vibrational spectroscopic approaches were also compared. The fit standard error between x-ray and ir secondary structure values estimated by our method is 2.5% for alpha-helix, 7.16% for beta structures, and 5.1% for other structures (turns and random coils). Quantitative results are given for the secondary structures (alpha-helix, turns, and beta-strands) present in the native state (turns and beta-strands up to now unknown in aqueous solution), together with the percentages of these structures and additional ones (random coils and beta-sheets) formed during denaturization.

Algorithms↗
PubMed1998

Predictions of protein secondary structures using factor analysis on Fourier transform infrared spectra: effect of Fourier self-deconvolution of the amide I and amide II bands.

Fourier self-deconvolution (FSD) was performed on protein amide I and II Fourier transform infrared (FTIR) spectra to test if the resultant increased band shape variation would lead to improvements in protein secondary structure prediction with our factor analysis based restricted multiple regression (RMR) methods. FTIR spectra of 23 proteins dissolved in H2O were measured and normalized to a constant amide I peak absorbance. The deconvolved spectra were renormalized by area so that the deconvolved spectra sets had the same area as before. Principal component analysis of the deconvolved spectra sets was carried out, which was followed by a selective multiple linear regression (RMR) analysis of the principal component loadings with regard to the fractional components (FC) of secondary structure. As compared to analyses based on the original spectra set, helix and sheet predictions were not noticeably improved by FSD; but, if a very large number of component spectra (16) were retained in the pool to select which loadings to be used in the RMR optimization, better predictions of turn and "other" resulted. The prediction quality varied depending on the deconvolution parameters used.

Animals↗
PubMed2000

Spectroscopic lineshape correction by QUECC: combined QUALITY deconvolution and eddy current correction.

Lineshape distortion due to residual eddy currents and magnetic field inhomogeneities are often present in short echo time (1)H spectroscopic data. Lineshape correction methods such as QUALITY deconvolution and eddy current correction (ECC), which use a separate reference spectrum for lineshape correction, have shortcomings when unsuppressed water is chosen as the reference. This paper outlines a method of integrating both techniques to overcome these limitations while still using unsuppressed water as the reference signal. This hybrid lineshape correction technique (QUECC) is demonstrated in vivo using stimulated echo acquisition mode (STEAM) localized 4.0 Tesla data. Metabolite quantification precision increased by an average of 7%-46% compared to QUALITY deconvolution (depending on filtering) and by an average of 6% compared to ECC.

Humans↗
PubMed2006

Image adaptive point-spread function estimation and deconvolution for in vivo confocal microscopy.

Visualizing deep inside the tissue of a thick biological sample often poses severe constraints on image conditions. Standard restoration techniques (denoising and deconvolution) can then be very useful, allowing one to increase the signal-to-noise ratio and the resolution of the images. In this paper, we consider the problem of obtaining a good determination of the point-spread function (PSF) of a confocal microscope, a prerequisite for applying deconvolution to three-dimensional image stacks acquired with this system. Because of scattering and optical distortion induced by the sample, the PSF has to be acquired anew for each experiment. To tackle this problem, we used a screening approach to estimate the PSF adaptively and automatically from the images. Small PSF-like structures were detected in the images, and a theoretical PSF model reshaped to match the geometric characteristics of these structures. We used numerical experiments to quantify the sensitivity of our detection method, and we demonstrated its usefulness by deconvolving images of the hearing organ acquired in vitro and in vivo.

Algorithms↗
PubMed2006

Richardson-Lucy algorithm with total variation regularization for 3D confocal microscope deconvolution.

Confocal laser scanning microscopy is a powerful and popular technique for 3D imaging of biological specimens. Although confocal microscopy images are much sharper than standard epifluorescence ones, they are still degraded by residual out-of-focus light and by Poisson noise due to photon-limited detection. Several deconvolution methods have been proposed to reduce these degradations, including the Richardson-Lucy iterative algorithm, which computes maximum likelihood estimation adapted to Poisson statistics. As this algorithm tends to amplify noise, regularization constraints based on some prior knowledge on the data have to be applied to stabilize the solution. Here, we propose to combine the Richardson-Lucy algorithm with a regularization constraint based on Total Variation, which suppresses unstable oscillations while preserving object edges. We show on simulated and real images that this constraint improves the deconvolution results as compared with the unregularized Richardson-Lucy algorithm, both visually and quantitatively.

Algorithms↗
PubMed1987

Evaluation of in vivo drug release by numerical deconvolution using oral solution data as weighting function.

Determination of in vivo drug release using compartmental model analysis is hampered by problems such as flip-flop phenomena and vanishing exponential terms. The usefulness of numerical deconvolution to estimate in vivo drug release was evaluated in this study by means of simulated data comparing solid dosage forms with a solution as a reference standard. Concentration-time data were generated using the standard linear two-compartment body model with various first-order release and absorption rate constants. Random errors of 5 and 10% were added to data sets for further analysis. The results of the study using error-free data afforded excellent agreement with the theoretical values except in one case where the release rate constant was overestimated by 6%. When random error was added to the data, the resulting in vivo release profile showed considerable fluctuation and no single rate constant could be assigned. However, further analysis showed that the method does not create additional error during the calculating process, as previously suggested, but merely reflects the inherent error added to the raw data. If the raw data are poor, no useful information can be obtained without using an arbitrary technique such as smoothing or fitting. In this regard, the time course of drug release obtained after numerical deconvolution merits investigation.

Absorption↗