Investigation of bias-free positioning estimators for the scintillation cameras

Joung, Jinhun; Miyaoka, Robert S.; Kohlmyer, S.G.; Lewellen, T.K.

doi:10.1109/23.940152

Cited by 21 publications

(8 citation statements)

References 11 publications

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“…The position is determined from the scintillation light distribution that is measured by photomultiplier tubes (PMTs). To ex- tract this information from the measured light distribution, maximum-likelihood methods are used [7]- [10].…”

Section: Introductionmentioning

confidence: 99%

Neural network-based position estimators for PET detectors using monolithic LSO blocks

Bruyndonckx

Léonard

Tavernier

et al. 2004

IEEE Trans. Nucl. Sci.

117

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The impinging position of a 511 keV photon onto a continuous scintillator can be obtained from the light distribution measured by a pixelated photodetector such as avalanche photodiode (APD) arrays. This information is extracted using neural networks trained for events with a particular incidence angle. Using a 20 10 10 mm block of lutetium oxyorthosilicate mounted onto a S8550 Hamamatsu APD matrix we achieved an intrinsic resolution of 1.9 mm full-width at half-maximum (FWHM) for perpendicular incident photons and 2.6 mm FWHM at a 40 incidence angle. A possible implementation for tomographic imaging is presented. Index Terms-Depth of interaction (DOI), neural network (NN), positron emission tomorgraphy (PET).

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Section: Introductionmentioning

confidence: 99%

Neural network-based position estimators for PET detectors using monolithic LSO blocks

Bruyndonckx

Léonard

Tavernier

et al. 2004

IEEE Trans. Nucl. Sci.

117

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show abstract

“…The position is determined from the generated light distribution that is measured by PMTs. To extract this information from the measured light distribution, maximum likelihood methods are used [7][8][9][10].…”

mentioning

confidence: 99%

Parallax recovery using neural networks in PET detectors based on continuous scintillators

Bryundonckx¹,

Léonard²,

Tavernier³

2003

2003 IEEE Nuclear Science Symposium. Conference Record (IEEE Cat. No.03CH37515)

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The photon incidence position onto a continuous scintillator can be obtained from the light distribution measured by a pixilated photon detector such as APD arrays. This information is extracted using a series of neural networks trained for events with a particular incidence angle. Using a 20x10x10 mm block of LSO mounted onto a S8550 Hamamatsu APD matrix we achieved an intrinsic resolution of 1.9 mm FWHM for perpendicular incident photons and 2.6 mm FWHM at a 40 o incidence angle 0-7803-8257

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“…Sin embargo, estos métodos no tienen en cuenta la fluctuación estadística de los fotones γ ni las no linealidades en la respuesta de los PMTs en función de la posición de impacto. Como consecuencia presenta serios problemas de distorsión espacial [170], que son más acusados en las proximidades de los bordes del cristal, produciéndose los denominados efectos de borde. Los efectos de borde son fácilmente apreciables: los eventos correspondientes al borde del cristal se detectan sistemáticamente en una posición más cercana al centro del cristal de lo que les corresponde, con lo que se obtiene una imagen comprimida.…”

Section: La Lógica De Angerunclassified

“…Posteriormente, el método fue mejorado utilizando información sobre el coeficiente de correlación lineal (LCC) [170]. La estimación de la posición se determina maximizando el coeficiente de correlación lineal entre la función verdadera y los datos medidos.…”

Section: Método Del Coeficiente De Correlación Linealunclassified

Redes neuronales y preprocesado de variables para modelos y sensores en bioingeniería

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Investigation of bias-free positioning estimators for the scintillation cameras

Cited by 21 publications

References 11 publications

Neural network-based position estimators for PET detectors using monolithic LSO blocks

Neural network-based position estimators for PET detectors using monolithic LSO blocks

Parallax recovery using neural networks in PET detectors based on continuous scintillators

Redes neuronales y preprocesado de variables para modelos y sensores en bioingeniería

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