Noise analysis of scintillation camera images: stochastic and non-stochastic effects

Grossman, Laurence W.; Anderson, Matthew P.; Jennings, Robert J.; Kruger, James B.; Lukes, S J; Wagner, Robert F.; Warr, Charles P.

doi:10.1088/0031-9155/31/9/001

Cited by 16 publications

(8 citation statements)

References 11 publications

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“…6͒, and will contain noise in every frequency. 33 Count statistics available in short time ͑100 or 500 ms͒ sinogram voxels are extremely limited. This is the reason that PET scans are acquired over minutes and this is the main obstacle a databased method for gating must address.…”

Section: Discussionmentioning

confidence: 99%

A new fast and fully automated software based algorithm for extracting respiratory signal from raw PET data and its comparison to other methods

Kesner

Kuntner

2010

Medical Physics

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PET data inherently contains information about patient motion; information that is not currently being utilized. We have shown that a respiratory signal can be extracted from raw PET data in potentially real-time and in a fully automated manner. This signal correlates well with hardware based signal for a large percentage of scans, and avoids the efforts and complications associated with hardware. The proposed method to extract a respiratory signal can be implemented on existing scanners and, if properly integrated, can be applied without changes to routine clinical procedures.

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Section: Discussionmentioning

confidence: 99%

A new fast and fully automated software based algorithm for extracting respiratory signal from raw PET data and its comparison to other methods

Kesner

Kuntner

2010

Medical Physics

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“…The stochastic NPS components can be found by subtracting S avg (u,v) from S tot (u,v). 28 Table I summarizes the methods for isolating spectral components due to these three types of noise.…”

Section: Stochastic and Nonstochastic Componentsmentioning

confidence: 99%

Noise power spectra of images from digital mammography detectors

1999

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Noise characterization through estimation of the noise power spectrum (NPS) is a central component of the evaluation of digital x-ray systems. We begin with a brief review of the fundamentals of NPS theory and measurement, derive explicit expressions for calculation of the one- and two-dimensional (1D and 2D) NPS, and discuss some of the considerations and tradeoffs when these concepts are applied to digital systems. Measurements of the NPS of two detectors for digital mammography are presented to illustrate some of the implications of the choices available. For both systems, two-dimensional noise power spectra obtained over a range of input fluence exhibit pronounced asymmetry between the orthogonal frequency dimensions. The 2D spectra of both systems also demonstrate dominant structures both on and off the primary frequency axes indicative of periodic noise components. Although the two systems share many common noise characteristics, there are significant differences, including markedly different dark-noise magnitudes, differences in NPS shape as a function of both spatial frequency and exposure, and differences in the natures of the residual fixed pattern noise following flat fielding corrections. For low x-ray exposures, quantum noise-limited operation may be possible only at low spatial frequency. Depending on the method of obtaining the 1D NPS (i.e., synthetic slit scanning or slice extraction from the 2D NPS), on-axis periodic structures can be misleadingly smoothed or missed entirely. Our measurements indicate that for these systems, 1D spectra useful for the purpose of detective quantum efficiency calculation may be obtained from thin cuts through the central portion of the calculated 2D NPS. On the other hand, low-frequency spectral values do not converge to an asymptotic value with increasing slit length when 1D spectra are generated using the scanned synthetic slit method. Aliasing can contribute significantly to the digital NPS, especially near the Nyquist frequency. Calculation of the theoretical presampling NPS and explicit inclusion of aliased noise power shows good agreement with measured values.

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“…For example, at spatial frequencies below 0.5/cm, the noise power spectrum may be dominated by nonstochastic noise [37]. In the clinical situation, the reviewer is disturbed by background noise in the region of interest, originating from uptake of activity in other organs, blood, and soft tissue.…”

Section: Discussionmentioning

confidence: 99%

Comparison of gamma (Anger) camera systems in terms of detective quantum efficiency using Monte Carlo simulation

Eriksson

Starck²,

Båth³

2014

Nuclear Medicine Communications

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Noise analysis of scintillation camera images: stochastic and non-stochastic effects

Cited by 16 publications

References 11 publications

A new fast and fully automated software based algorithm for extracting respiratory signal from raw PET data and its comparison to other methods

A new fast and fully automated software based algorithm for extracting respiratory signal from raw PET data and its comparison to other methods

Noise power spectra of images from digital mammography detectors

Comparison of gamma (Anger) camera systems in terms of detective quantum efficiency using Monte Carlo simulation

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