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PubMed1988

Deconvolution of emission tomographic data: a clinical evaluation.

A method of improving the quality of images in single photon emission computed tomography (SPECT) is demonstrated using transaxial images of the liver and brain. Deconvolution of the nuclear medicine data by a point source response function (PSRF) acquired previously in a scattering medium attempts to compensate for scattered radiation within the patient. The average geometric response of the collimator of the gamma camera is also compensated for with this technique. Three patients with known metastatic lesions in the liver and three with primary lesions in the brain were imaged. Clinical assessment of reconstructed slices both before and after deconvolution demonstrates that compensating for the effects of scatter and of collimator blurring leads to enhanced detail of pathological lesions. In all cases, cold lesions seen prior to deconvolution were enhanced in detail and, in addition, new lesions were seen with this technique.

Adult↗
PubMed1994

Robust, noniterative, and computationally efficient modification of van Cittert deconvolution optical figuring.

A modification of van Cittert deconvolution (VCD) is introduced and shown to yield a robust, noniterative (or closed-form), and numerically efficient method of deconvolution. This modification removes the restrictions limiting the applicability of conventional VCD only to shapes and relative positions of the convolved functions for which it converges, while also avoiding the ill effects of zeros in these functions. The resulting method is computationally efficient because it is noniterative and uses the fast Fourier transform. In contrast to the convergences obtained with VCD, those obtained with this modified method are ensured by their expansion in terms of an introduced auxiliary function rather than the convolved functions. This permits both the general removal of the above limitations and arbitrarily accurate deconvolution of infinitely sampled input data even in the presence of input data noise. For discretely sampled input data the accuracy of this modification is shown to be limited only by the implicit bandwidth of the input data density. To exemplify its numerical and analytical advantages, I apply the method to computer control of optical surface figuring. I also demonstrate an intrinsic means of optimal frequency filtering of raw input data made available by this modification. The advantages of this procedure are also applicable to image restoration.

Computer Simulation↗
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↗
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↗
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↗
PubMed1989

Numerical stability of pharmacokinetic deconvolution algorithms.

The sensitivity of pharmacokinetic deconvolution algorithms with respect to simulated experimental error has been studied. Approximations of cumulative absorption profiles reconstructed from simulated data sets with normally distributed random error were compared with corresponding known input functions. The coincidence of both functions was assessed in 600 numerical experiments by the nonparametric Kolmogorov-Smirnov test. A constrained iterative nonlinear regression procedure based on the analytical convolution of multiphasic zero-order input functions with linear disposition models was found to perform well under conditions where the results of direct numerical deconvolution and model-based mass balance methods were unsatisfactory.

Absorption↗
PubMed1992

Adaptive computer program for determination of absorption profiles by numerical deconvolution: application to amoxicillin absorption.

We have developed a specific numerical deconvolution program for the Apple Macintosh microcomputer. After comparison with other methods, we used the program to evaluate the influence of nifedipine on the absorption and bioavailability of amoxicillin. The technique provided a model-independent approach. This study shows that the simultaneous administration of nifedipine with amoxicillin leads to a significant increase in both the total quantity of amoxicillin absorbed (+22%) and the rate of absorption. Parameters of clearance, volume of distribution, and elimination were unaffected. Numerical deconvolution analysis showed that nifedipine did not modify the absorption kinetics of amoxicillin, which are characterized by a lag time followed by a constant rate of absorption, suggesting zero-order kinetics with first-order kinetics at the end of the process. The results suggest the existence of a specialized, saturable transport molecule for this antibiotic.

Adult↗
PubMed1994

Deconvolution method for assessing the absorption of a drug with reversible metabolic pathways.

A method has been established to determine the input rate for a drug with reversible metabolic processes. This method is based on linear system properties without imposing a compartment model for describing the disposition of the drug and the metabolite and their interconnections. The solution is a deconvolution method in N-dimensional space. A general solution has been obtained for calculating the parameters needed to describe the input function. The specific solution for a staircase input function (point-area deconvolution) is explicitly derived. Using a staircase input function is not a robust method and can give negative input values when applied to simulated data sets with a large amount of variability. This suggests the need for more robust functional forms to describe the input function.

Absorption↗
PubMed1994

Automated processing for proton spectroscopic imaging using water reference deconvolution.

Automated formation of MR spectroscopic images (MRSI) is necessary before routine application of these methods is possible for in vivo studies; however, this task is complicated by the presence of spatially dependent instrumental distortions and the complex nature of the MR spectrum. A data processing method is presented for completely automated formation of in vivo proton spectroscopic images, and applied for analysis of human brain metabolites. This procedure uses the water reference deconvolution method (G. A. Morris, J. Magn. Reson. 80, 547(1988)) to correct for line shape distortions caused by instrumental and sample characteristics, followed by parametric spectral analysis. Results for automated image formation were found to compare favorably with operator dependent spectral integration methods. While the water reference deconvolution processing was found to provide good correction of spatially dependent resonance frequency shifts, it was found to be susceptible to errors for correction of line shape distortions. These occur due to differences between the water reference and the metabolite distributions.

Artifacts↗
PubMed1992

Differentiation between transmembrane helices and peripheral helices by the deconvolution of circular dichroism spectra of membrane proteins.

The interpretation of the circular dichroism (CD) spectra of proteins to date requires additional secondary structural information of the proteins to be analyzed, such as X-ray or NMR data. Therefore, these methods are inappropriate for a CD database whose secondary structures are unknown, as in the case of the membrane proteins. The convex constraint analysis algorithm (Perczel, A., Hollósi, M., Tusnády, G., & Fasman, G. D., 1991, Protein Eng. 4, 669-679), on the other hand, operates only on a collection of spectral data to extract the common spectral components with their spectral weights. The linear combinations of these derived "pure" CD curves can reconstruct the original data set with great accuracy. For a membrane protein data set, the five-component spectra so obtained from the deconvolution consisted of two different types of alpha helices (the alpha helix in the soluble domain and the alpha T helix, for the transmembrane alpha helix), a beta-pleated sheet, a class C-like spectrum related to beta turns, and a spectrum correlated with the unordered conformation. The deconvoluted CD spectrum for the alpha T helix was characterized by a positive red-shifted band in the range 195-200 nm (+95,000 deg cm2 dmol-1), with the intensity of the negative band at 208 nm being slightly less negative than that of the 222-nm band (-50,000 and -60,000 deg cm2 dmol-1, respectively) in comparison with the regular alpha helix, with a positive band at 190 nm and two negative bands at 208 and 222 nm with magnitudes of +70,000, -30,000, and -30,000 deg cm2 dmol-1, respectively.

Adenosine Triphosphatases↗