Geant4, a Monte Carlo Simulation Toolkit extensively used in bio-medical physics, is in continuous evolution to include newest research findings to improve its accuracy and to respond to the evolving needs of a very diverse user community. In 2014, the G4-Med benchmarking system was born from the effort of the Geant4 Medical Simulation Benchmarking Group, to benchmark and monitor the evolution of Geant4 for medical physics applications. The G4-Med system was first described in our Medical Physics Special Report published in 2021. Results of the tests were reported for Geant4 10.5. In this work, we describe the evolution of the G4-Med benchmarking system. The G4-Med benchmarking suite currently includes 23 tests, which benchmark Geant4 from the calculation of basic physical quantities to the simulation of more clinically relevant set-ups. New tests concern the benchmarking of Geant4-DNA physics and chemistry components for regression testing purposes, dosimetry for brachytherapy with a 125 I $^{125}I$ source, dosimetry for external x-ray and electron FLASH radiotherapy, experimental microdosimetry for proton therapy, and in vivo PET for carbon and oxygen beams. Regression testing has been performed between Geant4 10.5 and 11.1. Finally, a simple Geant4 simulation has been developed and used to compare Geant4 EM physics constructors and physics lists in terms of execution times. In summary, our EM tests show that the parameters of the multiple scattering in the Geant4 EM constructor G4EmStandardPhysics_option3 in Geant4 11.1, while improving the modeling of the electron backscattering in high atomic number targets, are not adequate for dosimetry for clinical x-ray and electron beams. Therefore, these parameters have been reverted back to those of Geant4 10.5 in Geant4 11.2.1. The x-ray radiotherapy test shows significant differences in the modeling of the bremsstrahlung process, especially between G4EmPenelopePhysics and the other constructors under study ( G4EmLivermorePhysics , G4EmStandardPhysics_option3 , and G4EmStandardPhysics_option4 ). These differences will be studied in an in-depth investigation within our Group. Improvement in Geant4 11.1 has been observed for the modeling of the proton and carbon ion Bragg peak with energies of clinical interest, thanks to the adoption of ICRU90 to calculate the low energy proton stopping powers in water and of the Linhard–Sorensen ion model, available in Geant4 since version 11.0. Nuclear fragmentation tests of interest for carbon ion therapy show differences between Geant4 10.5 and 11.1 in terms of fragment yields. In particular, a higher production of boron fragments is observed with Geant4 11.1, leading to a better agreement with reference data for this fragment. Based on the overall results of our tests, we recommend to use G4EmStandardPhysics_option4 as EM constructor and QGSP_BIC_HP with G4EmStandardPhysics_option4 , for hadrontherapy applications. The Geant4-DNA physics lists report differences in modeling electron interactions in water, however, the tests have a pure regression testing purpose so no recommendation can be formulated.

BackgroundGeant4, a Monte Carlo Simulation Toolkit extensively used in bio-medical physics, is in continuous evolution to include newest research findings to improve its accuracy and to respond to the evolving needs of a very diverse user community. In 2014, the G4-Med benchmarking system was born from the effort of the Geant4 Medical Simulation Benchmarking Group, to benchmark and monitor the evolution of Geant4 for medical physics applications. The G4-Med system was first described in our Medical Physics Special Report published in 2021. Results of the tests were reported for Geant4 10.5.PurposeIn this work, we describe the evolution of the G4-Med benchmarking system.MethodsThe G4-Med benchmarking suite currently includes 23 tests, which benchmark Geant4 from the calculation of basic physical quantities to the simulation of more clinically relevant set-ups. New tests concern the benchmarking of Geant4-DNA physics and chemistry components for regression testing purposes, dosimetry for brachytherapy with a 125I$<^>{125}I$ source, dosimetry for external x-ray and electron FLASH radiotherapy, experimental microdosimetry for proton therapy, and in vivo PET for carbon and oxygen beams. Regression testing has been performed between Geant4 10.5 and 11.1. Finally, a simple Geant4 simulation has been developed and used to compare Geant4 EM physics constructors and physics lists in terms of execution times.ResultsIn summary, our EM tests show that the parameters of the multiple scattering in the Geant4 EM constructor G4EmStandardPhysics_option3 in Geant4 11.1, while improving the modeling of the electron backscattering in high atomic number targets, are not adequate for dosimetry for clinical x-ray and electron beams. Therefore, these parameters have been reverted back to those of Geant4 10.5 in Geant4 11.2.1. The x-ray radiotherapy test shows significant differences in the modeling of the bremsstrahlung process, especially between G4EmPenelopePhysics and the other constructors under study (G4EmLivermorePhysics, G4EmStandardPhysics_option3, and G4EmStandardPhysics_option4). These differences will be studied in an in-depth investigation within our Group. Improvement in Geant4 11.1 has been observed for the modeling of the proton and carbon ion Bragg peak with energies of clinical interest, thanks to the adoption of ICRU90 to calculate the low energy proton stopping powers in water and of the Linhard-Sorensen ion model, available in Geant4 since version 11.0. Nuclear fragmentation tests of interest for carbon ion therapy show differences between Geant4 10.5 and 11.1 in terms of fragment yields. In particular, a higher production of boron fragments is observed with Geant4 11.1, leading to a better agreement with reference data for this fragment.ConclusionsBased on the overall results of our tests, we recommend to use G4EmStandardPhysics_option4 as EM constructor and QGSP_BIC_HP with G4EmStandardPhysics_option4, for hadrontherapy applications. The Geant4-DNA physics lists report differences in modeling electron interactions in water, however, the tests have a pure regression testing purpose so no recommendation can be formulated.

BACKGROUND AND AIM:Microbeam Radiation Therapy (MRT) is an innovative radiotherapy modality which uses highly focused synchrotron-generated X-ray microbeams. Current pre-clinical research in MRT mostly rely on Monte Carlo (MC) simulations for dose estimation, which are highly accurate but computationally intensive. Recently, Deep Learning (DL) dose engines have been proved effective in generating fast and reliable dose distributions in different RT modalities. However, relatively few studies compare different models on the same task. This work aims to compare a Graph-Convolutional-Network-based DL model, developed in the context of Very High Energy Electron RT, to the Convolutional 3D U-Net that we recently implemented for MRT dose predictions. METHODS:The two DL solutions are trained with 3D dose maps, generated with the MC-Toolkit Geant4, in rats used in MRT pre-clinical research. The models are evaluated against Geant4 simulations, used as ground truth, and are assessed in terms of Mean Absolute Error, Mean Relative Error, and a voxel-wise version of the γ-index. Also presented are specific comparisons of predictions in relevant tumor regions, tissues boundaries and air pockets. The two models are finally compared from the perspective of the execution time and size. RESULTS:This study finds that the two models achieve comparable overall performance. Main differences are found in their dosimetric accuracy within specific regions, such as air pockets, and their respective inference times. Consequently, the choice between models should be guided primarily by data structure and time constraints, favoring the graph-based method for its flexibility or the 3D U-Net for its faster execution.

Background and aim: Cosmic rays have the potential to induce significant changes in atmospheric chemical reactions by generating ions, thereby influencing the atmosphere's chemical composition. The use of particle- molecule interaction models that account for the molecular structure of the atmospheric medium can advance our understanding on the role of ions, and enables a quantitative analysis of the impact of ion-molecule reactions on atmospheric modifications. This study marks the initial effort to expand the Geant-DNA toolkit for atmospheric applications. Methods: Building on our previous work, we extended the validation of new electron impact interaction models with the nitrogen and oxygen molecules up to 10 MeV. Additionally, we introduce electron cross sections for the carbon dioxide molecule, due to its crucial role as a major greenhouse gas. We present the implementation of the cross section models in Geant4-DNA, along with their validation through simulations of stopping power and range. Results: The differential cross sections have been verified against analytical calculations, demonstrating good agreement with existing literature data for all three molecules. The implementation has been validated through simulations of stopping power and range in N2, O2, CO2, and air. Results demonstrate agreement within 6% compared to reference data from the ESTAR database. Conclusions: The cross section models for the N2, O2, and CO2 atmospheric molecules have been implemented in the Geant4-DNA toolkit. This evolution is crucial for studying ionic reactive chemical networks in a quantitative manner, assessing the impact of ionization on chemical reactions occurring in the atmosphere and their implications for climate.

IntroductionExternal beam radiotherapy (RT) is one of the most common treatments against cancer, with photon-based RT and particle therapy being commonly employed modalities. Very high energy electrons (VHEE) have emerged as promising candidates for novel treatments, particularly in exploiting the FLASH effect, offering potential advantages over traditional modalities.MethodsThis paper introduces a Deep Learning model based on graph convolutional networks to determine dose distributions of therapeutic VHEE beams in patient tissues. The model emulates Monte Carlo (MC) simulated doses within a cylindrical volume around the beam, enabling high spatial resolution dose calculation along the beamline while managing memory constraints.ResultsTrained on diverse beam orientations and energies, the model exhibits strong generalization to unseen configurations, achieving high accuracy metrics, including a δ-index 3% passing rate of 99.8% and average relative error <1% in integrated dose profiles compared to MC simulations.DiscussionNotably, the model offers three to six orders of magnitude increased speed over full MC simulations and fast MC codes, generating dose distributions in milliseconds on a single GPU. This speed could enable direct integration into treatment planning optimization algorithms and leverage the model’s differentiability for exact gradient computation.

Cosmic rays have the potential to significantly affect the atmospheric composition by increasing the rate and changing the types of chemical reactions through ion production. The amount and states of ionization, and the spatial distribution of ions produced are still open questions for atmospheric models. To precisely estimate these quantities, it is necessary to simulate particle-molecule interactions, down to very low energies. Models enabling such simulations require interaction probabilities over a broad energy range and for all energetically allowed scattering processes. In this paper, we focus on electron interaction with the two most abundant molecules in the atmosphere, i.e., N2 and O2, as an initial step. A set of elastic and inelastic cross section models for electron transportation in oxygen and nitrogen molecules valid in the energy range 10 eV - 1 MeV, is presented. Comparison is made with available theoretical and experimental data and a reasonable good agreement is observed. Stopping power is calculated and compared with published data to assess the general consistency and reliability of our results. Good overall agreement is observed, with relative differences lower than 6% with the ESTAR database.

Indoor radon levels in dwellings are typically higher in cold months than in warm ones. The indoor radon concentration might experience an inverse seasonal behaviour - i.e., radon levels much higher in summer than in winter - under specific circumstances. In the framework of a study on long-term variations of annual radon concentration carried out in some tens of dwellings in Rome and surrounding small towns, two dwellings with very high - up to extreme - reverse seasonal variations were accidently discovered. These dwellings were located in a volcanic area, and they are both south-oriented and located on the lower part of a hill. In one of them, radon concentration was monitored by a continuous radon monitor for two years to find out when the greatest rises in radon levels occur. The indoor radon concentration resulted to experience extremely rapid, i.e. very few hours, increases up to 20 000 Bq m- 3 during the spring period (i.e., April, May, and June especially). After about ten years from the first observation, the indoor radon concentration of the same house was monitored again for about five years: radon concentration peaks previously observed were found to be unchanged in terms of absolute values, duration, rising time and occurrence period. These reverse seasonal variations may lead to significant underestimation of the actual annual average radon concentration in case of measurements lasting less than one year if performed during the cold season and especially when seasonal correction factors are used. Moreover, these results suggest adopting specific mea-surement protocol and remediation strategies in houses having some peculiar characteristics, mainly regarding orientation, position, and attachment to the ground.

Graphs are versatile structures for the representation of many real-world data. Deep Learning on graphs is currently able to solve a wide range of problems with excellent results. However, both the generation of graphs and the handling of large graphs still remain open challenges. This work aims to introduce techniques for generating large graphs and test the approach on a complex problem such as the calculation of dose distribution in oncological radiotherapy applications. To this end, we introduced a pooling technique (ReNN-Pool) capable of sampling nodes that are spatially uniform without computational requirements in both model training and inference. By construction, the ReNN-Pool also allows the definition of a symmetric un-pooling operation to recover the original dimensionality of the graphs. We also present a Variational AutoEncoder (VAE) for generating graphs, based on the defined pooling and un-pooling operations, which employs convolutional graph layers in both encoding and decoding phases. The performance of the model was tested on both the realistic use case of a cylindrical graph dataset for a radiotherapy application and the standard benchmark dataset sprite. Compared to other graph pooling techniques, ReNN-Pool proved to improve both performance and computational requirements.

Recent evidence indicates that transcranial ultrasound stimulation (TUS) modulates sensorimotor cortex excitability. However, no study has assessed possible TUS effects on the excitability of deeper brain areas, such as the brainstem. In this study, we investigated whether TUS delivered on the substantia nigra, superior colliculus, and nucleus raphe magnus modulates the excitability of trigeminal blink reflex, a reliable neurophysiological technique to assess brainstem functions in humans. The recovery cycle of the trigeminal blink reflex (interstimulus intervals of 250 and 500 ms) was tested before (T0), and 3 (T1) and 30 min (T2) after TUS. The effects of substantia nigra-TUS, superior colliculus-TUS, nucleus raphe magnus-TUS and sham-TUS were assessed in separate and randomized sessions. In the superior colliculus-TUS session, the conditioned R2 area increased at T1 compared with T0, while T2 and T0 values did not differ. Results were independent of the interstimulus intervals tested and were not related to trigeminal blink reflex baseline (T0) excitability. Conversely, the conditioned R2 area was comparable at T0, T1, and T2 in the nucleus raphe magnus-TUS and substantia nigra-TUS sessions. Our findings demonstrate that the excitability of brainstem circuits, as evaluated by testing the recovery cycle of the trigeminal blink reflex, can be increased by TUS. This result may reflect the modulation of inhibitory interneurons within the superior colliculus.

Working Group (WG) 6 'Computational Dosimetry' of the European Radiation Dosimetry Group promotes good practice in the application of computational methods for radiation dosimetry in radiation protection and the medical use of ionising radiation. Its cross-sectional activities within the association cover a large range of current topics in radiation dosimetry, including more fundamental studies of radiation effects in complex systems. In addition, WG 6 also performs scientific research and development as well as knowledge transfer activities, such as training courses. Monte Carlo techniques, including the use of anthropomorphic and other numerical phantoms based on voxelised geometrical models, play a strong part in the activities pursued in WG 6. However, other aspects and techniques, such as neutron spectra unfolding, have an important role as well. A number of intercomparison exercises have been carried out in the past to provide information on the accuracy with which computational methods are applied and whether best practice is being followed. Within the exercises that are still ongoing, the focus has changed towards assessing the uncertainty that can be achieved with these computational methods. Furthermore, the future strategy of WG 6 also includes an extension of the scope toward experimental benchmark activities and evaluation of cross-sections and algorithms, with the vision of establishing a gold standard for Monte Carlo methods used in medical and radiobiological applications.

The Italian National Institute of Health (Istituto Superiore di Sanità ‒ ISS) has a long experience of activities concerning protection from radon exposure (measuring techniques, surveys in dwellings and other environments, public information, training courses, epidemiological studies and risk evaluation, etc.) with the first activities dating back to the eighties. On the basis of this expertise, the ISS has also contributed to radon policies and regulations, in particular to the development of the first Italian National Radon Action Plan (INRAP) in 2002 and its coordination in the following years, as well as to international regulations and recommendations. Many activities have been carried out with significant collaborations not only with Italian agencies and institutes, but also with some other countries as well as with international organizations (e.g. WHO). In this paper, a short summary of the past activities carried out by the ISS on several radon issues is presented. Moreover, some of the recent and current activities and projects are shortly described, taking into account the requirements of the Council Directive 2013/59/Euratom, including design of radon surveys, quality of both active and passive measurements, evaluation of actual public exposure, and cost-effectiveness evaluations.

Purpose: A reliable model to simulate nuclear interactions is fundamental for Ion-therapy. We already showed how BLOB ("Boltzmann-Langevin One Body"), a model developed to simulate heavy ion interactions up to few hundreds of MeV/u, could simulate also $^{12}$C reactions in the same energy domain. However, its computation time is too long for any medical application. For this reason we present the possibility of emulating it with a Deep Learning algorithm. Methods: The BLOB final state is a Probability Density Function (PDF) of finding a nucleon in a position of the phase space. We discretised this PDF and trained a Variational Auto-Encoder (VAE) to reproduce such a discrete PDF. As a proof of concept, we developed and trained a VAE to emulate BLOB in simulating the interactions of $^{12}$C with $^{12}$C at 62 MeV/u. To have more control on the generation, we forced the VAE latent space to be organised with respect to the impact parameter ($b$) training a classifier of $b$ jointly with the VAE. Results: The distributions obtained from the VAE are similar to the input ones and the computation time needed to use the VAE as a generator is negligible. Conclusions: We show that it is possible to use a Deep Learning approach to emulate a model developed to simulate nuclear reactions in the energy range of interest for Ion-therapy. We foresee the implementation of the generation part in C++ and to interface it with the most used Monte Carlo toolkit: Geant4.

Uncertainty on long-term average radon concentration has a large impact on lung cancer risk assessment in epidemiological studies. The uncertainty can be estimated by year-to-year radon concentration variability, however few data are available. In Italy a study has been planned and conducted to evaluate year-to-year radon variability over several years in normally inhabited dwellings, mainly located in Rome. This is the longest study of this kind in Europe; repeat radon measurements are carried out for 10 years using LR-115 radon detectors in the same home in consecutive years. The study includes 84 dwellings with long-term average radon concentration ranging from 28 to 636 Bq/m3. The result shows that year-to-year variability of repeated measurements made in the same home in different years is low, with an overall coefficient of variation of 17%. This is smaller than most of those observed in studies from other European countries and USA, ranging from 15% to 62%. Influencing factors that may explain the differences between this study and other studies have been discussed. Due to the low yearly variability estimated in the present 10-year study, a negligible impact on lung cancer risk estimate for the Italian epidemiological study is expected.

The main goal of external beam radiotherapy (EBRT) is to damage all cancer cells in a tumour in such a way as to stop their proliferation. Unfortunately, normal tissue toxicity can severely limit doses used, and thus it is useful to combine EBRT with other treatments. An interesting approach to improving local tumour control is to combine EBRT with hyperthermia (HT). The cell sensitisation induced by the heat is not completely understood, however evidence suggests that it impairs cellular DNA repair, thus rendering radiation damage more lethal. During hyperthermia treatments the temperature distribution generated is crucial. High Intensity Therapeutic Ultrasound (HITU) provides superior control of energy deposition, and thus can increase the tumour temperature to the target level homogenously. One of the main goals in the Radiotherapy coupled with Hyperthermia (RaCHy) project is therefore to improve the efficacy, safety and range of applicability of clinical HITU treatments by providing validated methods for ultrasonic field characterization, HITU system performance testing, quality assurance and patient exposure monitoring. The RaCHy project will address both the basic calibration and specification of equipment ( and in particular characterisation of the acoustic pressure and temperature distribution in water and in-vitro and in-vivo experiments).