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Results for “deconvolution”

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At least 649 records · Page 36Linked to original sources
PubMed1997

Application of energy-resolved measurements to Laue diffraction: determination of unit-cell parameters, deconvolution of harmonics and assignment of systematic absences.

The use of energy-resolved area detection of Laue diffraction patterns for the determination of unit-cell parameters and systematic absences is demonstrated. Seven different crystals having previously known unit cells were re-examined using Laue diffraction methods. These crystals included four different crystal systems including cubic, orthorhombic, tetragonal and monoclinic cells. The crystals had cell sizes ranging from 179.4 to 4588.3 A(3). Comparison of known and re-determined cells showed good agreement (ratio of known to measured cells = 0.987 +/- 0.18). A single procedure was suitable for all unit-cell determinations. The accuracy of the method is presently limited by the quality of the available energy measurements. Some of the crystals represent space groups containing systematic absences normally obscured by harmonic overlap when using the Laue method. These include absences due to 2(1) screw axes (h, k or 1 = 2n + 1) and cell centering (h + k = 2n + 1). All systematic absences were identified using a combination of multiple linear regression with either stepwise elimination or stepwise inclusion and an F test for assignment of systematic absence. The methods are discussed in detail and simulations are used to evaluate critical tolerances for future systems.

Journal Article↗
PubMed2002

Deconvolution estimation of nerve conduction velocity distribution.

A conduction velocity distribution (CVD) estimator that incorporates volume conductor modeling of the nerve-evoked response is introduced in this paper. The CVD estimates are obtained from two compound nerve action potentials (CNAP) recorded at the skin surface. A third channel is introduced in order to assess the estimator performance in the experimental case. The relevance of using an accurate signal model is shown by comparing the performance of the proposed estimator with a previous approach based on a different CNAP model. The performance of the proposed estimator is evaluated for simulated and experimental data. The study assesses signal-to-noise ratio immunity and sensitivity to errors in the model parameters.

Action Potentials↗
PubMed2001

Bayesian 2-D deconvolution: a model for diffuse ultrasound scattering.

Observed medical ultrasound images are degraded representations of the true acoustic tissue reflectance. The degradation is due to blur and speckle and significantly reduces the diagnostic value of the images. To remove both blur and speckle, we have developed a new statistical model for diffuse scattering in 2-D ultrasound radio frequency images, incorporating both spatial smoothness constraints and a physical model for diffuse scattering. The modeling approach is Bayesian in nature, and we use Markov chain Monte Carlo methods to obtain the restorations. The results from restorations of some real and simulated radio frequency ultrasound images are presented and compared with results produced by Wiener filtering.

Algorithms↗
PubMed2001

Bayesian 2-D deconvolution: effect of using spatially invariant ultrasound point spread functions.

Observed ultrasound images are degraded representations of the-true tissue reflectance. The specular reflections at boundaries between regions of different tissue types are blurred, and the diffuse scattering within homogenous regions causes speckle because of the oscillating nature of the transmitted pulse. To reduce both blur and speckle, we have developed algorithms for the restoration of simulated and real ultrasound images based on Markov random field models and Bayesian statistical methods. The algorithm is summarized here, although a more detailed description can be found in our companion paper [1]. Because the point spread function (psf) is unknown, we investigate the effects of using incorrect frequencies and sizes for the model psf during the restoration process. First, we degrade the images either with a known simulated psf or a measured psf. Then, we use different psf shapes during restoration to study the robustness of the method. We found that small variations in the parameters characterizing the psf, less than +/- 25% change in frequency, width, or length, still yielded satisfactory results. When altering the psf more than this, the restorations were not acceptable. The restorations were particularly sensitive to large increases in the restoring psf frequency. Thus, 2-D Bayesian restoration using a fixed psf may yield acceptable results as long as the true variant psfs have not varied too much during imaging.

Bayes Theorem↗