This paper reviews the ecosystem of GATE, an open-source Monte Carlo toolkit for medical physics. Based on the shoulders of Geant4, the principal modules (geometry, physics, scorers) are described with brief descriptions of some key concepts (Volume, Actors, Digitizer). The main source code repositories are detailed together with the automated compilation and tests processes (Continuous Integration). We then described how the OpenGATE collaboration managed the collaborative development of about one hundred developers during almost 20 years. The impact of GATE on medical physics and cancer research is then summarized, and examples of a few key applications are given. Finally, future development perspectives are indicated.

Background: Radiopharmaceutical dosimetry depends on the localization in space and time of radioactive sources and requires the estimation of the amount of energy emitted by the sources deposited within targets. In particular, when computing resources are not accessible, this task can be carried out using precomputed tables of Specific Absorbed Fractions (SAFs) or S values based on dosimetric models. The OpenDose collaboration aims to generate and make freely available a range of dosimetric data and tools. Methods: OpenDose brings together resources and expertise from 18 international teams to produce and compare traceable dosimetric data using 6 of the most popular Monte Carlo codes in radiation transport (EGSnrc/EGS++, FLUKA, GATE, Geant4, MCNP/MCNPX and PENELOPE). SAFs are uploaded, together with their associated statistical uncertainties, in a relational database. S values are then calculated from mono-energetic SAFs, based on the radioisotope decay data presented in the International Commission on Radiological Protection (ICRP) publication 107. Results: The OpenDose collaboration produced SAFs for all source regions and targets combinations of the two ICRP 110 adult reference models. SAFs computed from the different Monte Carlo codes were in good agreement at all energies, with standard deviations below individual statistical uncertainties. Calculated S values were in good agreement with OLINDA 2 (commercial) and IDAC 2.1 (free) software. A dedicated website (www.opendose.org) has been developed to provide easy and open access to all data. Conclusion: The OpenDose website allows the display and download of SAFs and the corresponding S values for 1252 radionuclides. The OpenDose collaboration, open to new research teams, will extend data production to other dosimetric models and implement new free features, such as online dosimetric tools and patient-specific absorbed dose calculation software, together with educational resources.

Our goal was to determine the influence of sex, age and the head/brain size on the compartmental brain volumes in the radiologically verified healthy population (96 subjects; 54 women and 42 men) from the Upper Silesia region in Poland. The MRI examinations were done using 3T Philips Achieva with the same T1-weighted and T2-weighted protocols. The image segmentation procedures were performed with SPM (Statistical Parameter Mapping) and FSL-FIRST software. The volumes of 14 subcortical structures for the left and right hemispheres and 4 overall volumes were calculated. The General Linear Models (GLM) analysis was used with and without the Total Brain Volume (TBV) and Intracranial Volume (ICV) parameters as the covariates to study the regional vs. global brain atrophy. After the ICV/TBV adjustments, the majority of sex differences in the specific volumes of interest (VOIs) revealed to be linked to the difference in the head/brain size parameters. The analysis also confirmed the significant effect of the aging process on the brain loss. After the TBV adjustment, the age- and sex-related volumetric trends for the gray and white matter volumes were observed: the negative age dependence of the gray matter volume is more pronounced in the males, while in case of the white matter the positive age-related trend in the female group is weaker. The local losses of the left caudate nucleus and the right thalamus are more advanced than the global brain atrophy. Different head-size correction strategies are not interchangeable and may yield various volumetric results, but when used together, facilitate studies on the regional dependencies inherent to a healthy, but aging, brain.

Objective. This paper reports on the implementation and shows examples of the use of the ProTheRaMon framework for simulating the delivery of proton therapy treatment plans and range monitoring using positron emission tomography (PET). ProTheRaMon offers complete processing of proton therapy treatment plans, patient CT geometries, and intra-treatment PET imaging, taking into account therapy and imaging coordinate systems and activity decay during the PET imaging protocol specific to a given proton therapy facility. We present the ProTheRaMon framework and illustrate its potential use case and data processing steps for a patient treated at the Cyclotron Centre Bronowice (CCB) proton therapy center in Krakow, Poland. Approach. The ProTheRaMon framework is based on GATE Monte Carlo software, the CASToR reconstruction package and in-house developed Python and bash scripts. The framework consists of five separated simulation and data processing steps, that can be further optimized according to the user’s needs and specific settings of a given proton therapy facility and PET scanner design. Main results. ProTheRaMon is presented using example data from a patient treated at CCB and the J-PET scanner to demonstrate the application of the framework for proton therapy range monitoring. The output of each simulation and data processing stage is described and visualized. Significance. We demonstrate that the ProTheRaMon simulation platform is a high-performance tool, capable of running on a computational cluster and suitable for multi-parameter studies, with databases consisting of large number of patients, as well as different PET scanner geometries and settings for range monitoring in a clinical environment. Due to its modular structure, the ProTheRaMon framework can be adjusted for different proton therapy centers and/or different PET detector geometries. It is available to the community via github (Borys et al 2022).

Objective. The Jagiellonian positron emission tomography (J-PET) technology, based on plastic scintillators, has been proposed as a cost effective tool for detecting range deviations during proton therapy. This study investigates the feasibility of using J-PET for range monitoring by means of a detailed Monte Carlo simulation study of 95 patients who underwent proton therapy at the Cyclotron Centre Bronowice (CCB) in Krakow, Poland. Approach. Discrepancies between prescribed and delivered treatments were artificially introduced in the simulations by means of shifts in patient positioning and in the Hounsfield unit to the relative proton stopping power calibration curve. A dual-layer, cylindrical J-PET geometry was simulated in an in-room monitoring scenario and a triple-layer, dual-head geometry in an in-beam protocol. The distribution of range shifts in reconstructed PET activity was visualized in the beam’s eye view. Linear prediction models were constructed from all patients in the cohort, using the mean shift in reconstructed PET activity as a predictor of the mean proton range deviation. Main results. Maps of deviations in the range of reconstructed PET distributions showed agreement with those of deviations in dose range in most patients. The linear prediction model showed a good fit, with coefficient of determination r ² = 0.84 (in-room) and 0.75 (in-beam). Residual standard error was below 1 mm: 0.33 mm (in-room) and 0.23 mm (in-beam). Significance. The precision of the proposed prediction models shows the sensitivity of the proposed J-PET scanners to shifts in proton range for a wide range of clinical treatment plans. Furthermore, it motivates the use of such models as a tool for predicting proton range deviations and opens up new prospects for investigations into the use of intra-treatment PET images for predicting clinical metrics that aid in the assessment of the quality of delivered treatment.

Objective. Verification of delivered proton therapy treatments is essential for reaping the many benefits of the modality, with the most widely proposed in vivo verification technique being the imaging of positron emitting isotopes generated in the patient during treatment using positron emission tomography (PET). The purpose of this work is to reduce the computational resources and time required for simulation of patient activation during proton therapy using the GPU accelerated Monte Carlo code FRED, and to validate the predicted activity against the widely used Monte Carlo code GATE. Approach. We implement a continuous scoring approach for the production of positron emitting isotopes within FRED version 5.59.9. We simulate treatment plans delivered to 95 head and neck patients at Centrum Cyklotronowe Bronowice using this GPU implementation, and verify the accuracy using the Monte Carlo toolkit GATE version 9.0. Main results. We report an average reduction in computational time by a factor of 50 when using a local system with 2 GPUs as opposed to a large compute cluster utilising between 200 to 700 CPU threads, enabling simulation of patient activity within an average of 2.9 min as opposed to 146 min. All simulated plans are in good agreement across the two Monte Carlo codes. The two codes agree within a maximum of 0.95 σ on a voxel-by-voxel basis for the prediction of 7 different isotopes across 472 simulated fields delivered to 95 patients, with the average deviation over all fields being 6.4 × 10 ⁻³ σ . Significance. The implementation of activation calculations in the GPU accelerated Monte Carlo code FRED provides fast and reliable simulation of patient activation following proton therapy, allowing for research and development of clinical applications of range verification for this treatment modality using PET to proceed at a rapid pace.