Monte Carlo optimization of depth-of-interaction resolution in PET crystals

DeVol, Timothy A.; Moses, W.W.; Derenzo, Stephen E.

doi:10.1109/23.212336

Cited by 11 publications

(1 citation statement)

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“…The position-dependent light distribution has been used to measure the 511 keV photon interaction position in the crystal on an event by event basis to reduce radial elongation. 95 Different geometrical modifications were also simulated, leading to a proposal of a 2.2ϫ5ϫ30 mmBGO crystal, for which a 2.2 mm FWHM light distribution is predicted, which should yield a PET detector module with DOI measurement resolution of 3.6 mm FWHM. A test module with one 3ϫ3ϫ30 mmBGO crystal, one 3 mm square PIN photodiode and one PMT operated at Ϫ20°C with an amplifier peaking time of 4 s, and a measured DOI resolution of 5 to 8 mm FWHM has been proposed by Moses.…”

Section: A Detector Modelingmentioning

confidence: 99%

Relevance of accurate Monte Carlo modeling in nuclear medical imaging

1999

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Monte Carlo techniques have become popular in different areas of medical physics with advantage of powerful computing systems. In particular, they have been extensively applied to simulate processes involving random behavior and to quantify physical parameters that are difficult or even impossible to calculate by experimental measurements. Recent nuclear medical imaging innovations such as single-photon emission computed tomography ͑SPECT͒, positron emission tomography ͑PET͒, and multiple emission tomography ͑MET͒ are ideal for Monte Carlo modeling techniques because of the stochastic nature of radiation emission, transport and detection processes. Factors which have contributed to the wider use include improved models of radiation transport processes, the practicality of application with the development of acceleration schemes and the improved speed of computers. In this paper we present a derivation and methodological basis for this approach and critically review their areas of application in nuclear imaging. An overview of existing simulation programs is provided and illustrated with examples of some useful features of such sophisticated tools in connection with common computing facilities and more powerful multiple-processor parallel processing systems. Current and future trends in the field are also discussed.

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Section: A Detector Modelingmentioning

confidence: 99%