Abstract
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.
Highlights
Techniques based on Deep Learning have seen huge interest for several years showing, in particular, significant progress in computer vision
The article is structured in the following three parts: Sections 1.1 and 1.2 give a brief introduction of the principles of Monte Carlo simulation as well as deep learning, Section 2 presents a literature review in the context of medical physics, and Section 3 discusses on current challenges
Monte Carlo codes in medical physics are similar to those used in high energy physics community (HEP)
Summary
Techniques based on Deep Learning have seen huge interest for several years showing, in particular, significant progress in computer vision. Many medical applications have adopted them (see Shen et al [132] for a recent review) and a lot of research is currently underway These recent developments around Machine Learning in medical physics have found applications in the field. We will review and discuss the use of artificial intelligence, or machine Learning, for Monte Carlo simulation for particle transport especially in the context of medical physics. The article is structured in the following three parts: Sections 1.1 and 1.2 give a brief introduction of the principles of Monte Carlo simulation as well as deep learning, Section 2 presents a literature review in the context of medical physics, and Section 3 discusses on current challenges
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