2018
DOI: 10.1002/mp.13063
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Count statistics of nonparalyzable photon‐counting detectors with nonzero pulse length

Abstract: An analytical model for the count statistics of a nonparalyzable photon-counting detector with nonzero pulse length is presented. The model agrees well with results obtained from Monte Carlo simulations and can be used to improve, speed up and simplify modeling of photon-counting detectors.

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Cited by 10 publications
(9 citation statements)
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References 24 publications
(45 reference statements)
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“…where λ is the input count rate and τ is the dead time. The mean value and the variance of the output count rate for a non-paralyzable detector with non-zero pulse length, the so-called semi-nonparalyzable (SNP) model (Xu et al 2011), have been derived analytically (Grönberg et al 2018) and are given by…”
Section: Statistical Degradationmentioning
confidence: 99%
“…where λ is the input count rate and τ is the dead time. The mean value and the variance of the output count rate for a non-paralyzable detector with non-zero pulse length, the so-called semi-nonparalyzable (SNP) model (Xu et al 2011), have been derived analytically (Grönberg et al 2018) and are given by…”
Section: Statistical Degradationmentioning
confidence: 99%
“…These models can be seen as extreme cases of real counting systems, and present the advantage of disposing of exact analytical formulas describing the mean of the measured counts C out and their variance as a function of the input flux, as long as the input events are Poisson distributed. Other more elaborate models have been studied in the literature [4,5] but with no exact analytical expressions for their variance, and therefore have not been used in the study.…”
Section: Deadtime and Pile-up Modelsmentioning
confidence: 99%
“…However, since the problem of having too many incident photons during the same time window requires a very fast detector, we expect problems with pileup to be smaller compared to in current detectors. Regardless of this, pulse pileup can be included in the likelihood functions by incorporating a pileup model, see e.g., Grönberg et al 17 This will mainly result in using fluxdependent spectral response functions that relate the deposited energy to the incident photon energy to include spectral distortion. Further, the assumed count statistics will also be affected since pulse pileup results in non-Poissonian count statistics at high incident fluxes.…”
Section: Interaction Chainmentioning
confidence: 99%