2014
DOI: 10.1109/tmi.2014.2340251
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Reducing Multiplexing Artifacts in Multi-Pinhole SPECT With a Stacked Silicon-Germanium System: A Simulation Study

Abstract: In pinhole SPECT, multi-pinhole collimators can increase sensitivity but may lead to projection overlap, or multiplexing, which can cause image artifacts. In this work we explore whether a stacked-detector configuration with a germanium and a silicon detector, used with 123I (27–32, 159 keV), where little multiplexing occurs in the Si projections, can reduce image artifacts caused by highly-multiplexed Ge projections. Simulations are first used to determine a reconstruction method that combines the Si and Ge p… Show more

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Cited by 11 publications
(10 citation statements)
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“…The Wiener estimator not only has the lowest EMSE for all linear estimators when the data are Gaussian distributed, but it also only requires knowledge of the first- and second-order statistics of the raw image data[14]. Working with direct image data allowed us to bypass the traditional step of image reconstruction, which can introduce additional variables such as iteration number that further complicate the process of determining optimal system designs[19]. The Wiener estimate is given by θ^W=Kg,θKg1false(gg¯¯false)+θ¯,where K g,θ is the cross-covariance of the signal parameters and the data, K g is the data covariance, and g˭ is the mean noise-free image data averaged over Poisson noise and all object distributions.…”
Section: Methodsmentioning
confidence: 99%
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“…The Wiener estimator not only has the lowest EMSE for all linear estimators when the data are Gaussian distributed, but it also only requires knowledge of the first- and second-order statistics of the raw image data[14]. Working with direct image data allowed us to bypass the traditional step of image reconstruction, which can introduce additional variables such as iteration number that further complicate the process of determining optimal system designs[19]. The Wiener estimate is given by θ^W=Kg,θKg1false(gg¯¯false)+θ¯,where K g,θ is the cross-covariance of the signal parameters and the data, K g is the data covariance, and g˭ is the mean noise-free image data averaged over Poisson noise and all object distributions.…”
Section: Methodsmentioning
confidence: 99%
“…12,13 One major advantage of using 123 I is that it emits photons at various energies which allowed us to utilize the multienergy approach described before, with the silicon detector (Si), placed in front, being sensitive to the $ 30 keV photons and the germanium detector (Ge), in the back, being sensitive to the 159 keV photons. 10 The relative abundances of photons at these two energy ranges are relatively similar; however, the exact values were not modeled in our simulations. The germanium detector used in the system has an exquisite energy resolution, while maintaining similar spatial resolutions to conventional detectors.…”
Section: Introductionmentioning
confidence: 94%
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