The OpenGATE ecosystem for Monte Carlo simulation in medical physics

Sarrut, David; Arbor, N.; Baudier, Thomas; Borys, Damian; Etxebeste, Ane; Fuchs, H.; Gajewski, J.; Grevillot, L.; Jan, Sébastien; Kagadis, George C.; Kang, Han Gyu; Kirov, A.; Krzemień, W.; Lomax, Anthony; Papadimitroulas, Panagiotis; Pommranz, Christian; Roncali, Emilie; Ruciński, Antoni; Winterhalter, Carla; Maigne, Lydia

doi:10.1088/1361-6560/ac8c83

Cited by 33 publications

(23 citation statements)

References 148 publications

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“…Further developments will build on the presented results and continue progressing, with the final aim to integrate the optiGAN within the simulation toolkit GATE/Geant4 for the community to benefit from this new simulation approach. The optiGAN integration and use will be simplified and user-friendly since GATE will be directly managed by Python libraries (Sarrut et al 2022).…”

Section: Discussionmentioning

confidence: 99%

A generative adversarial network to speed up optical Monte Carlo simulations

Trigila

Srikanth

Roncali

2023

Mach. Learn.: Sci. Technol.

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Detailed simulation of optical photon transport and detection in radiation detectors is often used for crystal-based gamma detector optimization. However, the time and memory burden associated with the track-wise approach to particle transport and detection in commonly used Monte Carlo codes makes optical simulation prohibitive at a system level, where hundreds to thousands of scintillators must be modeled. Consequently, current large system simulations do not include detailed detector models to analyze the potential performance gain with new radiation detector technologies. Generative Adversarial Networks are explored as a tool to speed up the optical simulation of crystal-based detectors. These networks learn training datasets made of high-dimensional data distributions. Once trained, the resulting model can produce distributions belonging to the training data probability distribution. In this work, we present the proof of concept of using a Generative Adversarial Network to enable high-fidelity optical simulations of nuclear medicine systems, mitigating their computational complexity. The architecture of the first network version and high-fidelity training dataset is discussed. The latter is generated through accurate optical simulation with GATE/Geant4, and contains the position, direction, and energy distributions of the optical photons emitted by 511 keV gamma rays in bismuth germanate and detected on the photodetector face. We compare the GAN and simulation-generated distributions in terms of similarity using the Jensen-Shannon distance. Excellent agreement was found with similarity values higher than 93.5% for all distributions. Moreover, the GAN speeded the optical photon distribution generation by up to 2 orders of magnitude. These very promising results have the potential to drastically change the use of nuclear imaging system optical simulations by enabling high-fidelity system-level simulations in reasonable computation times. The ultimate is to integrate the GAN within GATE/Geant4 since numerous applications (large detectors, bright scintillators, Cerenkov-based timing positron emission tomography) can benefit from these improvements.

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Section: Discussionmentioning

confidence: 99%

A generative adversarial network to speed up optical Monte Carlo simulations

Trigila

Srikanth

Roncali

2023

Mach. Learn.: Sci. Technol.

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“…We used the ProTheRaMon software framework, in which the delivery of proton therapy treatment plans were simulated, scoring the β + activity produced by protons during the treatment, as well as the J-PET scanner response to the annihilation gammas (Borys et al 2022). The ProTheRaMon framework utilizes the GATE platform for Monte Carlo simulations (Sarrut et al 2022), the CASToR package for PET image reconstruction (Merlin et al 2018), and in-house developed Python and bash scripts. The framework consists of five separate simulation and data processing steps.…”

Section: Monte Carlo Simulationsmentioning

confidence: 99%

Feasibility of the J-PET to monitor range of therapeutic proton beams

Borys¹,

Brzeziński²,

Gajewski³

et al. 2023

Preprint

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Objective: The aim of this work is to investigate the feasibility of the Jagiellonian Positron Emission Tomography (J-PET) scanner for intra-treatment proton beam range monitoring.Approach: The Monte Carlo simulation studies with GATE and PET image reconstruction with CASToR were performed in order to compare six J-PET scanner geometries (three dual-heads and three cylindrical). We simulated proton irradiation of a PMMA phantom with a Single Pencil Beam (SPB) and Spread-Out Bragg Peak (SOBP) of various ranges. The sensitivity and precision of each scanner were calculated, and considering the setup's cost-effectiveness, we indicated potentially optimal geometries for the J-PET scanner prototype dedicated to the proton beam range assessment.Main results: The investigations indicate that the double-layer cylindrical and triple-layer double-head configurations are the most promising for clinical application. We found that the scanner sensitivity is of the order of 10 −5 coincidences per primary proton, while the precision of the range assessment for both SPB and SOBP irradiation plans was found below 1 mm. Among the scanners with the same number of detector modules, the best results are found for the triple-layer dual-head geometry.Significance: We performed simulation studies demonstrating that the feasibility of the J-PET detector for PET-based proton beam therapy range monitoring is possible with reasonable sensitivity and precision enabling its pre-clinical tests in the clinical proton therapy environment. Considering the sensitivity, precision and costeffectiveness, the double-layer cylindrical and triple-layer dual-head J-PET geometry configurations seem promising for the future clinical application. Experimental tests are needed to confirm these findings.

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“…The rate N 0 can be obtained by the product of the source activity and the fraction of its decay that emits gamma rays and positrons [30]. For instance, 22 Na and 18 F emit beta plus particles with a branching ratio of 0.9 and 0.97, respectively [1].…”

Section: Analytical Model Of Sensitivitymentioning

confidence: 99%

“…GATE describes time-dependent processes, acquisition and signal processing stages for a full system simulation. While GATE is the basic tool for simulation of both scintillation detectors and systems, and its specifications improved significantly over the years by an open source community [18], there are some inherent vulnerabilities in the use of simulations.…”

Section: Introductionmentioning

confidence: 99%

Analytical modeling of the sensitivity of cylindrical PET systems based on bulk materials and metascintillators

Bonifacio¹,

Latella²,

Murata³

et al. 2023

Preprint

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<p>Positron emission tomography scanners are commonly characterized by their photon sensitivity. Scanner design often requires Monte Carlo simulations to probe different geometries and materials. However, the computational load of such simulations can be significant and costly. Furthermore, the applicability of the Monte Carlo approach in optimization loops is limited as each instance, such as source position or scanner dimensions, has to be simulated independently.</p> <p>In this work, Monte Carlo results have been accurately replicated by an analytical model that uses characteristics of the foreseen cylindrical scanner and returns the sensitivity profile following NEMA guidelines. BGO and LYSO bulk materials and several metascintillator scenarios have been used. The mean absolute error (MAE), mean absolute percentage error (MAPE) and standard deviation of the error (SDE) are as low as 0.49%, 2.22% and 0.26% when no energy window is used, respectively. With an energy window applied, the analytical model presents the lowest values of MAE and SDE, with MAPE value being 8.19%. A normalization factor has been used to compensate for the scattered events included in the 350-650 keV window. This work facilitates significantly the development of cylindrical scanners, allowing direct probing of their axial sensitivity profiles.</p>

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The OpenGATE ecosystem for Monte Carlo simulation in medical physics

Cited by 33 publications

References 148 publications

A generative adversarial network to speed up optical Monte Carlo simulations

A generative adversarial network to speed up optical Monte Carlo simulations

Feasibility of the J-PET to monitor range of therapeutic proton beams

Analytical modeling of the sensitivity of cylindrical PET systems based on bulk materials and metascintillators

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