Generative adversarial networks (GAN) for compact beam source modelling in Monte Carlo simulations

Sarrut, David; Krah, Nils; Létang, J M

doi:10.1088/1361-6560/ab3fc1

Cited by 21 publications

(29 citation statements)

References 20 publications

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“…The reinforcement learning strategy was used to iterative methods the nonlinear relationship between MRI and CT, and to produce more realistic images [11] , [25] . The Medical Images (MI-GAN) emits synthetic medical images and their based segmentation masks which could be used to apply structured medical imaging analysis [26] .…”

Section: Medical Images Generative Adversarial Network (Mi-gan)mentioning

confidence: 99%

WITHDRAWN: Advanced machine learning-based analytics on COVID-19 data using generative adversarial networks

Kumar

Harshavardhan²,

Bhukya

et al. 2020

Materials Today: Proceedings

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The domain of medical diagnosis and predictive analytics is one of the key domains of research with enormous dimensions whereby the diseases of different types can be predicted. Nowadays, there is a huge panic of impact and rapid mutation of the COVID-19 virus impression. The world is getting affected by this virus to a huge extent and there is no vaccine developed so far. India is also having more than 10,000 patients with than 300 deceased. The global human community is having around 20 lacs of Coronavirus patients. The Generative Adversarial Network (GAN) is the contemporary high-performance approach in which the use of advanced neural networks is done for the cavernous analytics of the images and multimedia data. In this research work, the analytics of key points from medical images of the COVID-19 dataset is to be presented using which the diagnosis and predictions can be done for the patients. The GANs are used for the generation, transformation as well as presentation of the dataset and key points using advanced deep learning models which can analyze the patterns in the medical images including X-Ray, CT Scan, and many others. Using such approaches with the integration of GANs, the overall predictive analytics can be made high performance aware as compared to the classical neural networks with multiple layers. In this research manuscript, the inscription of work is projected on the benchmark datasets with the advanced scripting so that the predictive mining and knowledge discovery can be done effectively with more accuracy.

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Section: Medical Images Generative Adversarial Network (Mi-gan)mentioning

confidence: 99%

WITHDRAWN: Advanced machine learning-based analytics on COVID-19 data using generative adversarial networks

Kumar

Harshavardhan²,

Bhukya

et al. 2020

Materials Today: Proceedings

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show abstract

“…Recent works in the medical physics field have explored the use of generative networks, GANs in particular, to model particle source distributions and potentially speed up Monte Carlo simulations [125,127]. In the proposed methods, the training data set is a phase space file generated by an analog MC simulation which contains properties (energy, position, direction) of all particles reaching a specific surface.…”

Section: Ai For Monte Carlo Source Modellingmentioning

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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“…This would, however, have resulted in a very large phase space library because of the many degrees of freedom of the adaptive aperture and range modulator system. An interesting alternative to potentially investigate in the future would be the use of generative neural networks as recently proposed for phase space modeling of therapeutic linear accelerators [28]. We underline that FRED is not specific to the Mevion accelerator but can handle any other currently available proton therapy system if the beam line is properly implemented.…”

Section: Discussionmentioning

confidence: 99%

Implementation of a Compact Spot-Scanning Proton Therapy System in a GPU Monte Carlo Code to Support Clinical Routine

et al. 2020

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The purpose of this work was to implement a fast Monte Carlo dose calculation tool, Fred, in the Maastro proton therapy center in Maastricht (Netherlands) to complement the clinical treatment planning system. Fred achieves high accuracy and computation speed by using physics models optimized for radiotherapy and extensive use of GPU technology for parallelization. We implemented the beam model of the Mevion S250i proton beam and validated it against data measured during commissioning and calculated with the clinical TPS. The beam exits the accelerator with a pristine energy of around 230 MeV and then travels through the dynamically extendable nozzle of the device. The nozzle contains the range modulation system and the multi-leaf collimator system named adaptive aperture. The latter trims the spots laterally over the 20 × 20 cm2 area at the isocenter plane. We use a single model to parameterize the longitudinal (energy and energy spread) and transverse (beam shape) phase space of the non-degraded beam in the default nozzle position. The range modulation plates and the adaptive aperture are simulated explicitly and moved in and out of the simulation geometry dynamically by Fred. Patient dose distributions recalculated with Fred were comparable with the TPS and met the clinical criteria. Calculation time was on the order of 10–15 min for typical patient cases, and future optimization of the simulation statistics is likely to improve this further. Already now, Fred is fast enough to be used as a tool for plan verification based on machine log files and daily (on-the-fly) dose recalculations in our facility.

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Generative adversarial networks (GAN) for compact beam source modelling in Monte Carlo simulations

Cited by 21 publications

References 20 publications

WITHDRAWN: Advanced machine learning-based analytics on COVID-19 data using generative adversarial networks

WITHDRAWN: Advanced machine learning-based analytics on COVID-19 data using generative adversarial networks

Artificial Intelligence for Monte Carlo Simulation in Medical Physics

Implementation of a Compact Spot-Scanning Proton Therapy System in a GPU Monte Carlo Code to Support Clinical Routine

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