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Results for “deconvolution”

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At least 631 records · Page 35Linked to original sources
PubMed1995

Deconvolution of planar scintigrams by maximum entropy.

Planar scintigrams are deconvolved with a point spread function using the maximum entropy method with the aim of improving image quality. The technique requires the specification of several parameters. These are related to the level of noise present in the data and our a priori knowledge of the object imaged. The performance of the technique is tested for a wide range of these parameters using images of a Williams phantom in scattering material and a figure of merit, derived from the detectability of the smallest cold spot, is calculated. For close to optimal values of the parameters a factor of two improvement in the figure is found. A processed bone image shows improved contrast and resolution. Maximum entropy processing could be used to increase image quality or allow comparable image quality with reduced imaging time or patient dose.

Bone and Bones↗
PubMed2002

Deconvolution of detector size effect for output factor measurement for narrow Gamma Knife radiosurgery beams.

This paper presents the results of measurements of output factors (OFs) for a model U Gamma Knife collimator, with special emphasis on the accurate determination of the OF for the 4 mm collimator (OF4). In the past, the OF4 was set to 0.800 relative to the 18 mm collimator. Recently, the manufacturer has recommended a new value of 0.870 for OF4. However, most centres still use the old value of the OF4. In the present study, the Gamma Knife OFs were measured using a commercially available miniature diamond detector and a miniature 0.006 cc ion chamber, which was especially designed for the task. The measured OF4 were corrected for spatial averaging effects by measuring dose profiles for the 4 mm collimator with the same detectors and deconvolving their response from the measured profiles. A Gaussian kernel was used to describe the detector response. The relative OFs measured with the diamond detector/ion chamber were 0.986/0.982, 0.953/0.935 and 0.812/0.765 for the 14,8 and 4 mm collimators, respectively, as compared with the manufacturer's values of 0.984, 0.956 and 0.87. The corrected OF4 was 0.881 +/- 0.012 for the diamond detector and 0.851 +/- 0.012 for the ion chamber, supporting the manufacturer's revised value for this collimator.

Calibration↗
PubMed2004

Photoacoustic imaging with deconvolution algorithm.

The impulse response of the ultrasonic transducer used for detection is crucial for photoacoustic imaging with high resolution. We demonstrate a reconstruction method that allows the optical absorption distribution of a sample to be reconstructed without knowing the impulse response of the ultrasonic transducer. A convolution relationship between photoacoustic signals measured by an ultrasound transducer and optical absorption distribution is developed. Based on this theory, the projection of the optical absorption distribution of a sample can be obtained directly by deconvolving the recorded PA signal originating from a point source out of that from the sample. And a modified filtered back projection algorithm is used to reconstruct the optical absorption distribution. We constructed a photoacoustic imaging system to validate the reconstruction method and the experimental results demonstrated that the reconstructed images agreed well with the original phantom samples. The spatial resolution of the system reaches 0.3 mm.

Acoustics↗
PubMed2025

StrainR2 accurately deconvolutes strain-level abundances in synthetic microbial communities.

MOTIVATION: Synthetic microbial communities offer an opportunity to conduct reductionist research in tractable model systems. However, deriving abundances of highly related strains within these communities is currently unreliable. 16S rRNA gene sequencing does not resolve abundance at the strain level and other methods such as quantitative polymerase chain reaction (qPCR) scale poorly and are resource prohibitive for complex communities. We present StrainR2, which utilizes shotgun metagenomic sequencing to provide high accuracy strain-level abundances for all members of a synthetic community, provided their genomes. RESULTS: Both in silico, and using sequencing data derived from gnotobiotic mice colonized with a synthetic fecal microbiota, StrainR2 resolves strain abundances with greater accuracy and efficiency than other tools utilizing shotgun metagenomic sequencing reads. We demonstrate that StrainR2's accuracy is comparable to that of qPCR on a subset of strains resolved using absolute quantification. AVAILABILITY AND IMPLEMENTATION: Software is available at GitHub and implemented in C, R, and Bash. Software is supported on Linux and MacOS, with packages available on Bioconda or as a Docker container. The source code at the time of publication is also available on figshare at the doi: 10.6084/m9.figshare.29420780.

Mice↗
PubMed2003

Gene structure-based splice variant deconvolution using a microarray platform.

MOTIVATION: Alternative splicing allows a single gene to generate multiple mRNAs, which can be translated into functionally and structurally diverse proteins. One gene can have multiple variants coexisting at different concentrations. Estimating the relative abundance of each variant is important for the study of underlying biological function. Microarrays are standard tools that measure gene expression. But most design and analysis has not accounted for splice variants. Thus splice variant-specific chip designs and analysis algorithms are needed for accurate gene expression profiling. RESULTS: Inspired by Li and Wong (2001), we developed a gene structure-based algorithm to determine the relative abundance of known splice variants. Probe intensities are modeled across multiple experiments using gene structures as constraints. Model parameters are obtained through a maximum likelihood estimation (MLE) process/framework. The algorithm produces the relative concentration of each variant, as well as an affinity term associated with each probe. Validation of the algorithm is performed by a set of controlled spike experiments as well as endogenous tissue samples using a human splice variant array.

Algorithms↗
PubMed2026

transfactor: transcription factor activity estimation via probabilistic gene expression deconvolution.

Gene expression is a primary modality being studied to differentiate between biological cells. Contemporary single-cell studies simultaneously measure genome-wide transcription levels for thousands of individual cells in a single experiment. While the characterization of cell population differences has often occurred through differential gene expression analysis, tiny effect sizes become statistically significant when thousands of cells are available for each population, compromising biological interpretation. Moreover, these large studies have spurred the development of methods to infer gene regulatory networks (GRNs) directly from the data, and GRN databases are becoming more comprehensive. In this work, we propose a statistical model for gene expression measures and an inference method that leverage GRNs to deconvolve transcription factor (TF) activity from gene expression, by probabilistically assigning mRNA molecules to TFs. This shifts the paradigm from investigating gene expression differences to regulatory differences at the level of TF activity, aiding interpretation and allowing prioritization of a limited number of TFs responsible for significant contributions to the observed gene expression differences. The inferred TF activities result in intuitive prioritization of TFs in terms of the (difference in) estimated number of molecules they produce, in contrast to other widely used methods relying on arbitrary enrichment scores. Our model allows the incorporation of prior information on the regulatory potential between each TF and target gene and is able to deal with both repressing and activating interactions. We compare our approach to other TF activity estimation methods using two simulation experiments and two case studies. Single-cell RNA-sequencing; TF activity; bioinformatics; GRN.

Transcription Factors↗
PubMed1991

Convex constraint analysis: a natural deconvolution of circular dichroism curves of proteins.

A new algorithm, called convex constraint analysis, has been developed to deduce the chiral contribution of the common secondary structures directly from experimental CD curves of a large number of proteins. The analysis is based on CD data reported by Yang, J.T., Wu, C.-S.C. and Martinez, H.M. [Methods Enzymol., 130, 208-269 (1986)]. Application of the decomposition algorithm for simulated protein data sets resulted in component spectra [B (lambda, i)] identical to the originals and weights [C (i, k)] with excellent Pearson correlation coefficients (R) [Chang, C.T., Wu, C.-S.C. and Yang, J.T. (1978) Anal. Biochem., 91, 12-31]. Test runs were performed on sets of simulated protein spectra created by the Monte Carlo technique using poly-L-lysine-based pure component spectra. The significant correlational coefficients (R greater than 0.9) demonstrated the high power of the algorithm. The algorithm, applied to globular protein data, independent of X-ray data, revealed that the CD spectrum of a given protein is composed of at least four independent sources of chirality. Three of the computed component curves show remarkable resemblance to the CD spectra of known protein secondary structures. This approach yields a significant improvement in secondary structural evaluations when compared with previous methods, as compared with X-ray data, and yields a realistic set of pure component spectra. The new method is a useful tool not only in analyzing CD spectra of globular proteins but also has the potential for the analysis of integral membrane proteins.

Algorithms↗
PubMed2005

A computer program for the deconvolution of thermoluminescence glow curves.

A quick and efficient computer program was developed in order to resolve the peaks from the thermoluminescence (TL) glow curve. The program was designed to be easily used on any MS Windows-based computer with a graphical user interface. In this program, a new method based on the general one-trap TL equation was adopted to analyse the TL glow curve with the traditional first-order, second-order and general-order kinetics model. The method described here, general approximation, generates TL glow peaks and interpolates the relevant TL parameters from the glow data. The program was tested with simulated and experimental TL glow data and the results were found to be adequate and reliable.

Algorithms↗
PubMed1996

Assessment of radioactive systemic uptakes by deconvolution of individual monitoring results.

The methods of interpretation presently available for evaluating individual radioactivity intakes from measured data involve difficulties connected with the adaptation of metabolic models to the situations encountered in practice. These difficulties essentially concern the definition of appropriate parameters for each encountered case, and very often- except for characterized incidents-erroneous appreciation of the time course of contamination episodes and of th routes of entry. These considerations led us to develop a simplified method of interpreting monitoring data, by considering separately the data relating to routes of entry and those concerning systemic contamination, i.e., the contamination occurring after the transfer of radionuclides to the blood. An approach to interpreting measurements of systemic contamination is proposed in this study. This method is to calculate, from these measurements, the values for the activity absorbed daily from the routes of entry into the blood using the appropriate retention and excretion functions. a day-to-day follow-up of the absorbed activity becomes possible, thus enabling its real-time evolution to be recorded and easy to consult. A few applications of the method are described, including cases of acute tritium and uranium contamination and of chronic contamination by tritium, uranium, and iodine. The conditions and constraints required to validate the proposed approach are indicated.

Follow-Up Studies↗
PubMed1999

MAXED, a computer code for maximum entropy deconvolution of multisphere neutron spectrometer data.

Reliable neutron dosimetry requires knowledge of the neutron spectrum. We discuss the problem of analyzing data from a multisphere neutron spectrometer to infer the energy spectrum of the incident neutrons and describe the code MAXED, a computer program developed to apply the maximum entropy principle to this problem. The code and documentation are available from the authors upon request.

Documentation↗
PubMedDate not supplied

Tissue mean transit time from dynamic computed tomography by a simple deconvolution technique.

In order to calculate the mean transit time of tissue, such as brain, from dynamic computed tomography performed after a bolus injection of intravenous contrast material, the time dependence of the input of contrast material to the tissue must be "deconvolved" from the observed time course of the tissue contrast enhancement. If the approximate shape of the curve of the response of the tissue to an instantaneous injection of contrast material is assumed, the width of this curve that gives the best fit to the observed tissue response can be used to find a value for the tissue mean transit time. Applying this technique to dynamic CT scans of two normal volunteers yielded values comparable to those in the literature by other techniques. The method has the advantages of being simple to implement, relatively insensitive to noise and the details of the assumed curve shape, and not requiring any curve fitting to correct for recirculation.

Blood Circulation↗