Advanced Monte Carlo simulations of emission tomography imaging systems with GATE

Sarrut, David; Bała, M.; Bardiès, Manuel; Bert, Julien; Chauvin, Maxime; Chatzipapas, Konstantinos; Dupont, Mathieu; Etxebeste, Ane; Fanchon, Louise M.; Jan, Sébastien; Kayal, Gunjan; Kirov, A.; Kowalski, P.; Krzemień, W.; Labour, Joey; Lenz, Mirjam; Loudos, George; Mehadji, Brahim; Ménard, Laurent; Morel, C.; Papadimitroulas, Panagiotis; Rafecas, M.; Salvadori, Julien; Seiter, Daniel; Stockhoff, Mariele; Testa, É.; Trigila, Carlotta; Pietrzyk, U.; Vandenberghe, Stefaan; Verdier, M.-A.; Visvikis, Dimitris; Ziemons, K.; Zvolský, M.; Roncali, Emilie

doi:10.1088/1361-6560/abf276

Cited by 111 publications

(91 citation statements)

References 195 publications

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“…R&D activities in the field of Monte Carlo simulations have resulted in the development of generic computer codes, i.e. which allow the user to simulate a wide range of particles, energies, geometrical elements and physical interactions (EGSnrc [65], MCNPX [55], Penelope [120], Fluka [18], Geant4 [1,4], Gate [57,123,124], etc.). The accuracy of the underlying physical models and cross-section databases has continuously been improved, also thanks to new experimental data.…”

Section: Monte Carlo Modelling In Medical Physicsmentioning

confidence: 99%

Artificial Intelligence for Monte Carlo Simulation in Medical Physics

et al. 2021

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Monte Carlo simulation of particle tracking in matter is the reference simulation method in the field of medical physics. It is heavily used in various applications such as 1) patient dose distribution estimation in different therapy modalities (radiotherapy, protontherapy or ion therapy) or for radio-protection investigations of ionizing radiation-based imaging systems (CT, nuclear imaging), 2) development of numerous imaging detectors, in X-ray imaging (conventional CT, dual-energy, multi-spectral, phase contrast … ), nuclear imaging (PET, SPECT, Compton Camera) or even advanced specific imaging methods such as proton/ion imaging, or prompt-gamma emission distribution estimation in hadrontherapy monitoring. Monte Carlo simulation is a key tool both in academic research labs as well as industrial research and development services. Because of the very nature of the Monte Carlo method, involving iterative and stochastic estimation of numerous probability density functions, the computation time is high. Despite the continuous and significant progress on computer hardware and the (relative) easiness of using code parallelisms, the computation time is still an issue for highly demanding and complex simulations. Hence, since decades, Variance Reduction Techniques have been proposed to accelerate the processes in a specific configuration. In this article, we review the recent use of Artificial Intelligence methods for Monte Carlo simulation in medical physics and their main associated challenges. In the first section, the main principles of some neural networks architectures such as Convolutional Neural Networks or Generative Adversarial Network are briefly described together with a literature review of their applications in the domain of medical physics Monte Carlo simulations. In particular, we will focus on dose estimation with convolutional neural networks, dose denoising from low statistics Monte Carlo simulations, detector modelling and event selection with neural networks, generative networks for source and phase space modelling. The expected interests of those approaches are discussed. In the second section, we focus on the current challenges that still arise in this promising field.

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Section: Monte Carlo Modelling In Medical Physicsmentioning

confidence: 99%

Artificial Intelligence for Monte Carlo Simulation in Medical Physics

et al. 2021

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“…Based on computer programs developed by the PET-CT manufacturer, the method uses the same processes available on the physical systems. In comparison to some simulation studies [7][8][9], the processes rely on a real acquisition performed on the system.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of a Method Combining Physical Experiment and Data Insertion For the Assessment of PET-CT Systems

Maronnier

Courbon

Caselles

2021

Preprint

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Background: To evaluate and compare Positron Emission Tomography (PET) devices among them, tests are performed on phantoms that generally consist in simple geometrical objects, fillable with radiotracers. On one hand, those tests bring a control over the experiment through the operator preparation but on the other hand, they are limited in terms of reproducibility, repeatability and are time-consuming, in particular, if several replications are required. To overcome these restrictions, we designed a method combining physical experiment and data insertion that aims to avoid experimental repetitions while testing multiple configurations for the performance evaluation of PET scanners.Methods: Based on the National Electrical Manufacturers Association Image Quality standard, four experiments, with different spheres-to-background ratios: 2:1, 4:1, 6:1 and 8:1, were performed. An additional acquisition was done with a radioactive background and no activity within the spheres. It was created as a baseline to artificially simulate the radioactive spheres and reproduce initial experiments. Standard sphere set was replaced by smaller target sizes (4, 5, 6, 8, 10 and 13 mm) to match current detectability performance of PET scanners. Images were reconstructed following standard guidelines, i.e. using OSEM algorithm, and an additional BPL reconstruction was performed. We visually compared experimental and simulated images. We measured the activity concentration values into the spheres to calculate the mean and maximum recovery coefficient (RCmean and RCmax ) which we used in a quantitative analysis.Results: No significant visual discrepancies were identified between experimental and simulated series. Mann-Whitney U tests comparing simulated and experimental distributions showed no statistical differences for both RCmean (P value = 0.611) and RCmax (P value = 0.720). Spearman tests revealed high correlation for RCmean (ρ = 0.974, P value < 0.001) and RCmax (ρ = 0.974, P value < 0.001) between both datasets. According to Bland-Altman plots, we highlighted slight shifts in RCmean and RCmax of respectively 2.1 ± 16.9 % and 3.3 ± 22.3 %.Conclusions: The method produced realistic results compared to experimental data. Known synthesized information fused with original data allows full exploration of the system's capabilities while avoiding the limitations associated with repeated experiments.

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“…Calculations are preformed using the GATE software (Jan et al 2004, Sarrut et al 2014, 2021 which enables generation of back-to-back annihilation photons of the sources as defined by the NEMA norm, and simulation of interaction of these photons in the detector material. Using the dedicated analysis programs the list-mode set of coincidences is obtained (Kowalski et al 2015(Kowalski et al , 2016(Kowalski et al , 2018.…”

Section: Simulation Toolsmentioning

confidence: 99%

“…In post-processing, positions of interaction points in scintillators were smeared with the FWHM of 5 mm (along axis of the detecting chamber), while the TOF resolution was set to 135 ps (FWHM). All the simulations were performed with GATE (Sarrut et al 2014(Sarrut et al , 2021.…”

Section: Image Qualitymentioning

confidence: 99%

Simulating NEMA characteristics of the modular total-body J-PET scanner—an economic total-body PET from plastic scintillators

et al. 2021

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The purpose of the presented research is estimation of the performance characteristics of the economic total-body Jagiellonian-PET system (TB-J-PET) constructed from plastic scintillators. The characteristics are estimated according to the NEMA NU-2-2018 standards utilizing the GATE package. The simulated detector consists of 24 modules, each built out of 32 plastic scintillator strips (each with cross section of 6 mm times 30 mm and length of 140 or 200 cm) arranged in two layers in regular 24-sided polygon circumscribing a circle with the diameter of 78.6 cm. For the TB-J-PET with an axial field-of-view (AFOV) of 200 cm, a spatial resolutions (SRs) of 3.7 mm (transversal) and 4.9 mm (axial) are achieved. The noise equivalent count rate (NECR) peak of 630 kcps is expected at 30 kBq cc −1 . Activity concentration and the sensitivity at the center amounts to 38 cps kBq −1 . The scatter fraction (SF) is estimated to 36.2 %. The values of SF and SR are comparable to those obtained for the state-of-the-art clinical PET scanners and the first total-body tomographs: uExplorer and PennPET. With respect to the standard PET systems with AFOV in the range from 16 to 26 cm, the TB-J-PET is characterized by an increase in NECR approximately by factor of 4 and by the increase of the whole-body sensitivity by factor of 12.6 to 38. The time-of-flight resolution for the TB-J-PET is expected to be at the level of CRT = 240 ps full width at half maximum. For the TB-J-PET with an AFOV of 140 cm, an image quality of the reconstructed images of a NEMA IEC phantom was presented with a contrast recovery coefficient and a background variability parameters. The increase of the whole-body sensitivity and NECR estimated for the TB-J-PET with respect to current commercial PET systems makes the TB-J-PET a promising cost-effective solution for the broad clinical applications of total-body PET scanners. TB-J-PET may constitute an economic alternative for the crystal TB-PET scanners, since plastic scintillators are much cheaper than BGO or LYSO crystals and axial arrangement of the strips significantly reduces the costs of readout electronics and SiPMs.

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Advanced Monte Carlo simulations of emission tomography imaging systems with GATE

Cited by 111 publications

References 195 publications

Artificial Intelligence for Monte Carlo Simulation in Medical Physics

Artificial Intelligence for Monte Carlo Simulation in Medical Physics

Evaluation of a Method Combining Physical Experiment and Data Insertion For the Assessment of PET-CT Systems

Simulating NEMA characteristics of the modular total-body J-PET scanner—an economic total-body PET from plastic scintillators

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