使用生成对抗网络加速F-18正电子路径模拟
Fast-Track of F-18 Positron paths simulations using GANs
AI总结：
本研究提出一种条件变换器LS-GAN模型，用于快速模拟18F正电子路径，与GATE MC模拟相比，在多种材料中路径长度和PSF差异小于10%。
摘要：
近年来，蒙特卡洛（MC）模拟在医学物理领域的应用已成为一种先进技术，但为了准确预测粒子相互作用，它消耗了大量的计算资源。最近，生成对抗网络（GAN）被提出作为提高计算工具效率并扩展其在医学成像和治疗应用中适用性的替代方案。本研究介绍了一种利用GAN模拟源自氟18（18F）同位素的正电子路径的新方法。所提出的方法开发了一个纯条件变换器最小二乘（LS）-GAN模型，旨在生成正电子路径并追踪其与周围材料的相互作用。条件因素包括预定的相互作用次数，以及根据18F发射谱得出的发射正电子的初始动量。通过利用这些条件，该模型旨在快速准确地模拟正电子路径的电磁相互作用。将结果与使用Geant4应用断层发射（GATE）MC模拟工具包产生的结果进行了比较。在三种不同材料（水、骨、肺）中，路径的平均长度和最大长度以及一维点扩散函数（PSF）的计算差异小于10%。
英文摘要：
In recent years, the use of Monte Carlo (MC) simulations in the domain of Medical Physics has become a state-of-the-art technology that consumes lots of computational resources for the accurate prediction of particle interactions. The use of generative adversarial network (GAN) has been recently proposed as an alternative to improve the efficiency and extending the applications of computational tools in both medical imaging and therapeutic applications. This study introduces a new approach to simulate positron paths originating from Fluorine 18 (18 F) isotopes through the utilization of GANs. The proposed methodology developed a pure conditional transformer least squares (LS)-GAN model, designed to generate positron paths, and to track their interaction within the surrounding material. Conditioning factors include the pre-determined number of interactions, and the initial momentum of the emitted positrons, as derived from the emission spectrum of 18 F. By leveraging these conditions, the model aims to quickly and accurately simulate electromagnetic interactions of positron paths. Results were compared to the outcome produced with Geant4 Application for Tomography Emission (GATE) MC simulations toolkit. Less than 10 % of difference was observed in the calculation of the mean and maximum length of the path and the 1-D point spread function (PSF) for three different materials (Water, Bone, Lung).
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