Validation of a small-animal PET simulation using GAMOS: a GEANT4-based framework

Abstract

Monte Carlo-based modelling is a powerful tool to help in the design and optimization of positrón emission tomography (PET) systems. The performance of fhese systems depends on several parameters, such as detector physical characteristics, shielding or electronics, whose effects can be studied on the basis of realistic simulated data. The aim of this paper is to validate a comprehensive study of the Raytest ClearPET small-animal PET scanner using a new Monte Cario simulation platform which has been developed at CIEMAT (Madrid, Spain), called GAMOS (GEANT4-based Architecture for Medicine-Oriented Simulations). This toolkit, based on the GEANT4 code, was originally designed to cover múltiple applications in the fleld of medical physics from radiotherapy to nuclear medicine, but has since been applied by some of its users in other flelds of physics, such as neutrón shielding, space physics, high energy physics, etc. Our simulation model includes the relevant characteristics of the ClearPET system, namely, the double layer of scintillator crystals in phoswich conflguration, the rotating gantry, the presence of intrinsic radioactivity in the crystals or the storage of single events for an offline coincidence sorting. Simulated results are contrasted with experimental acquisitions including studies of spatial resolution, sensitivity, scatter fraction and count rates in accordance with the National Electrical Manufacturers Association (NEMA) NU 4-2008 protocol. Spatial resolution results showed a discrepancy between simulated and measured valúes equal to 8.4% (with a máximum FWHM difference over all measurement directions of 0.5 mm). Sensitivity results differ less than 1% for a 250-750 keV energy window. Simulated and measured count rates agree well within a wide range of activities, including under electronic saturation of the system (the measured peak of total coincidences, for the mouse-sized phantom, was 250.8 kcps reached at 0.95 MBq ruL" 1 and the simulated peak was 247.1 kcps at 0.87 MBq niL" 1 ). Agreement better than 3% was obtained in the scatter fraction comparison study.We also measured and simulated a mini-Derenzo phantom obtaining images with similar quality using iterative reconstruction methods. We concluded that the overall performance of trie simulation showed good agreement with the measured results and validates the GAMOS package for PET applications. Furthermore, its ease of use and flexibility recommends it as an excellent tool to optimize design features or image reconstruction tecfmiques.

Recent developments in Geant4

“…Further validations come from the medical and space communities, in particular, GATE [138], GAMOS [139], GRAS [140], and TOPAS [141]. There are also many validation results obtained by single user groups.…”

A GAMOS plug-in for GEANT4 based Monte Carlo simulation of radiation-induced light transport in biological media

“…However, the GEANT4 architecture for medically oriented simulations (GAMOS) provides a framework for users to easily interface with GEANT4 using only text-based scripts [24]. The GAMOS project has reported almost one thousand registered users and has been validated by several groups for various radiation-based applications [25,26]. In addition, while GEANT4 includes a set of optical photon physics processes, to the best of the authors’ knowledge simulation of diffuse light transport in GEANT4 has neither been fully characterized nor validated.…”

An alternative approach to histopathological validation of PET imaging for radiation therapy image-guidance: A proof of concept

“…However, in case of larger resection specimens (>1 cm), and situations where the specimen is positioned away from the centre of the field of view, using a variable convolution kernel (spatially dependant on position inside the scanner bore) could be required in order to provide a reasonable approximation of the PET image. An even more accurate approach to obtaining sPET images would be to use readily available Monte Carlo models for clinical and research PET scanners [25–27] that simulate the physical processes involved in PET image formation in a specific PET scanner. Starting with a given heterogeneous source of activity, such as a 3D autoradiography microscopic tracer uptake map, these models can be utilised to generate the raw data that can be used to reconstruct corresponding realistic PET images, taking into account the variable spatial resolution of the acquisition system.…”