Digital twins in dosimetry and radiotherapy, a survey and some applications

Abstract

Digital Twins (DTs) are emerging as a powerful tool in several applications, including Dosimetry and Radiotherapy (RT). Indeed, DTs have increased their effectiveness, up to the point that can be considered as virtual replicas of physical objects, processes, or systems. Through predictive modeling, DTs can determine optimal doses in radiation therapy, assist in defining personalized treatment schedules for individual patients, and monitor treatment responses. Moreover, DTs play a pivotal role in designing radioisotope production processes in the field of nuclear medicine. This can help to ensure the most effective treatment possible while minimizing the risk of side effects.As a frontier case example, DTs can be employed in the analysis of a possible new source for flash radiotherapy worth with respect to their effectiveness parameters like Radio-Biological Effectiveness (RBE) or cell Surviving Fraction (SF). Flash techniques involve the delivery of a high dose of radiation in a short time, reducing damage to surrounding healthy tissue while possibly increasing damage to malignant cells. DTs could be used to simulate and collect data about particle interactions with the treated tissues, optimizing the treatment and leading to improved outcomes.The most powerful tools that can be applied in DTs development are the Monte Carlo (MC) codes, particularly those with the highest capability of a detailed material/geometry description through an automatized spatial discretization approach like the one based on Unstructured Mesh (UM), allowing a common mathematical domain for both particle/radiation transport and thermal, fluid-dynamics and mechanical simulations. What comes out from various kind of applications is a common ground in terms of data management, phase space identification, simulation tools, that can be easily recognized even if spanning from cell response to technological devices, making realistic the perspective of the setup of general optimization procedures.