Development of a new MC Fast Dose Engine for small compact radiation fields generated by the CyberKnifeⓇ Iris medical linac

Abstract

MC-based computerized TPS is an essential tool for improving treatment quality in external radiation therapy chains. However, incorporating these systems into clinical TPS is challenging due to their long computation time. To address this problem, Phase Spaces (PhSp) can be utilized, which reduce the computation time of such simulations while maintaining accuracy levels comparable to full simulations. Since PhSps can only generate a finite number of particles, recycling the same PhSp several times during a simulation will increase statistical uncertainty. To carry out this disadvantage, the present study employs a numerical reconstruction model to develop a novel generation of MC Fast Dose Engine (MC-FDE) that relies on the Cyberknife linac parameters publicly available in the IAEA database. The MC-FDE model’s performance is evaluated based on the precision of the reconstructed parameters and the accuracy of dose calculation in the 3D BEAMSCAN water phantom and patient CT datasets. In contrast to the recently published Wasserstein Generative Adversarial Networks (WGAN), the MC-FDE reconstruction model produces significant agreement with measured data, IAEA PhSp files and TPS Accuray, as determined by marginal distributions, correlation metrics, 2-3D gamma indexes and the Dose Volume Histogram (DVH) test. Regarding calculation speed, MC-FDE has a very fast process and can generate an infinite number of particles, unlike the limited number of particles recorded in IAEA PhSp files. According to the findings of this study, the proposed MC-FDE model is considered suitable for use as part of clinical TPS.