Experimental characterization of monolithic-crystal small animal PET detectors read out by APD arrays

Maas, Marnix C.; Laan, D.J. van der; Schaart, Dennis R.; Huizenga, J.; Brouwer, J.C.; Bruyndonckx, Peter; Léonard, Sarah; Lemaître, Cédric; Eijk, C.W.E. van

doi:10.1109/tns.2006.873711

Cited by 72 publications

(59 citation statements)

References 15 publications

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“…It can be shown that as L → ∞ and L/N → 0 (with N the number of reference distributions), the algorithm performs optimally (Webb 2002). In practice, N < ∞, so a value of L < N that gives good performance has to be found, e.g., as described by Maas et al (2006).…”

Section: Description Of the Pet Detectormentioning

confidence: 99%

Optical simulation of monolithic scintillator detectors using GATE/GEANT4

et al. 2010

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Much research is being conducted on position-sensitive scintillation detectors for medical imaging, particularly for emission tomography. Monte Carlo simulations play an essential role in many of these research activities. As the scintillation process, the transport of scintillation photons through the crystal(s), and the conversion of these photons into electronic signals each have a major influence on the detector performance; all of these processes may need to be incorporated in the model to obtain accurate results. In this work the optical and scintillation models of the GEANT4 simulation toolkit are validated by comparing simulations and measurements on monolithic scintillator detectors for high-resolution positron emission tomography (PET). We have furthermore made the GEANT4 optical models available within the user-friendly GATE simulation platform (as of version 3.0). It is shown how the necessary optical input parameters can be determined with sufficient accuracy. The results show that the optical physics models of GATE/GEANT4 enable accurate prediction of the spatial and energy resolution of monolithic scintillator PET detectors.

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Section: Description Of the Pet Detectormentioning

confidence: 99%

Optical simulation of monolithic scintillator detectors using GATE/GEANT4

et al. 2010

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“…As of very recently, SiPMs can be manufactured into compact arrays that can be used as position-sensitive light sensors in PET detectors (España et al 2008, Kolb et al 2008, Llosá et al 2008, Schaart et al 2008b. The aim of this paper is to present an initial characterization of the first SiPM-arraybased PET detector following the monolithic scintillator concept explored using APD arrays by Maas et al (2006Maas et al ( , 2009. This monolithic approach has previously been shown to allow not only high resolution and excellent DOI correction but also very high system sensitivity …”

Section: Introductionmentioning

confidence: 99%

A novel, SiPM-array-based, monolithic scintillator detector for PET

et al. 2009

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Silicon photomultipliers (SiPMs) are of great interest to positron emission tomography (PET), as they enable new detector geometries, for e.g., depthof-interaction (DOI) determination, are MR compatible, and offer faster response and higher gain than other solid-state photosensors such as avalanche photodiodes. Here we present a novel detector design with DOI correction, in which a position-sensitive SiPM array is used to read out a monolithic scintillator. Initial characterization of a prototype detector consisting of a 4 × 4 SiPM array coupled to either the front or back surface of a 13.2 mm × 13.2 mm × 10 mm LYSO:Ce 3+ crystal shows that front-side readout results in significantly better performance than conventional back-side readout. Spatial resolutions <1.6 mm full-width-at-half-maximum (FWHM) were measured at the detector centre in response to an ∼0.54 mm FWHM diameter test beam. Hardly any resolution losses were observed at angles of incidence up to 45• , demonstrating excellent DOI correction. About 14% FWHM energy resolution was obtained. The timing resolution, measured in coincidence with a BaF 2 detector, equals 960 ps FWHM.

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“…These numbers were obtained by linear interpolation of the histograms. This position estimation algorithm and its performance optimization are described in more detail elsewhere [36]. In the present work, the position estimation algorithm was operated using 500 nearest neighbors.…”

Section: ) Spatial Resolutionmentioning

confidence: 99%