A deconvolution program for processing radiotracer dilution curves

Commenges, Daniel; Brendel, A. J.

doi:10.1016/0010-468x(82)90033-2

Cited by 20 publications

(4 citation statements)

References 6 publications

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“…For example, in biomedicine for the study of vascular impulse responses (Commenges and Brendel 1982), in radar (Riek et al 1990), for oil and gas re¯ection seismology (Robinson 1980), in astrophysics (Walden and Williams 1993) and in the equalization of digital communications systems (Li 1995).…”

Section: Introductionmentioning

confidence: 99%

Trispectrum deconvolution of linear processes with randomly missing observations

Gabr¹,

Al-Ibrahim²

2002

International Journal of Systems Science

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In a large application area of time series analysis ± geophysical exploration ± the underlying innovations sequence is of primary interest and must be estimated. This sequence is estimated by deconvolving the non-Gaussian stationary time series. The deconvolution of a non-Gaussian non-minimum phase linear process, when some observations are missing according to a point binomial distribution is considered. This analysis tools are the bispectrum and the trispectrum. A Monte Carlo study is performed to illustrate the proposed methods.

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Section: Introductionmentioning

confidence: 99%

Trispectrum deconvolution of linear processes with randomly missing observations

Gabr¹,

Al-Ibrahim²

2002

International Journal of Systems Science

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“…1(a), which is the average estimated power spectrum of the three seismic traces given in Fig. in astrophysics for examining the optical variation of a Quasar (Scargle, 1981), in biomedicine in the study of vascular impulse responses (Commenges & Brendel, 1982) and in radar applications (Riek, Crane & O'Neill, 1990). Noise dominates the spectrum from about 75 Hz onwards, but is about 15 dB lower than in the signal-dominated pass-band of 10-50 Hz.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Statistical Bandwidth of a Time Series

Walden¹,

White²

1990

Biometrika

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JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. Biometrika Trust is collaborating with JSTOR to digitize, preserve and extend access to Biometrika. SUMMARY In many time series and signal processing applications such as spectral estimation, signal-to-noise estimation and frequency response estimation it is imperative to have a good estimate of the bandwidth of the time series. In many scientific applications deconvolution is carried out over the signal-dominated part of the spectrum resulting in a spectrum which is dominantly unimodal, and hence a technique like that used for smoothing windows can be used to define the statistical bandwidth of the data. This paper demonstrates, using bandwidths and data lengths of interest in seismic data investigations, that the naive estimator of data bandwidths using an untapered autocovariance is extremely biased. A method for removing the bias is found and its success demonstrated on synthetic and real data. If the tapered sample autocovariance function is used in the estimator then the best truncation point corresponds to a ratio of smoothing window bandwidth to data bandwidth in the range 02-025, well below the recommended upper bound for spectrum estimation of 0 5.

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“…Since that time different approaches have been reported in the analysis of time-activity data in order to find a reliable index related to the size of the shunt (Folse and Braunwald 1962, Tian et al , Anderson et al 1974. Among all these methods the one proposed by Maltz and Treves (1973) has been most accepted (Commenges and Brendel 1982, Alderson 1980, Bowyer and Starmer 1982. According to Maltz and Treves it is possible to determine the pulmonary to systemic flow ratio by analysing the pulmonary timeactivity curve using a gamma-variate model.…”

Section: Introductionmentioning

confidence: 99%

Theoretical aspects of multiple deconvolution analysis for quantification of left to right cardiac shunts

1987

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A new method for quantification of left to right cardiac shunts by Bourguignon et al based on multiple deconvolution analysis is critically analysed within the framework of a simple mathematical model. Underlying assumptions are explicitly stated and their validity discussed. It turns out that some reinterpretation of the method is necessary. Using the same ideas as in multiple deconvolution analysis, a new relation for the pulmonary to systemic flow ratio is proposed on a theoretical basis. This technique may be useful when diagnosing left to right cardiac shunts with radiocardiographic methods.

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A deconvolution program for processing radiotracer dilution curves

Cited by 20 publications

References 6 publications

Trispectrum deconvolution of linear processes with randomly missing observations

Trispectrum deconvolution of linear processes with randomly missing observations

Estimating the Statistical Bandwidth of a Time Series

Theoretical aspects of multiple deconvolution analysis for quantification of left to right cardiac shunts

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