A review of the use and potential of the GATE Monte Carlo simulation code for radiation therapy and dosimetry applications

Framework for evaluating photon-counting detectors under pile-up conditions

Purpose

While X-ray photon-counting detectors (PCDs) promise to revolutionize medical imaging, theoretical frameworks to evaluate them are commonly limited to incident fluence rates sufficiently low that the detector response can be considered linear. However, typical clinical operating conditions lead to a significant level of pile-up, invalidating this assumption of a linear response. Here, we present a framework that aims to evaluate PCDs, taking into account their non-linear behavior.

Approach

We employ small-signal analysis to study the behavior of PCDs under pile-up conditions. The response is approximated as linear around a given operating point, determined by the incident spectrum and fluence rate. The detector response is subsequently described by the proposed perturbation point spread function (pPSF). We demonstrate this approach using Monte-Carlo simulations of idealized direct- and indirect-conversion PCDs.

Results

The pPSFs of two PCDs are calculated. It is then shown how the pPSF allows to determine the sensitivity of the detector signal to an arbitrary lesion. This example illustrates the detrimental influence of pile-up, which may cause non-intuitive effects such as contrast/contrast-to-noise ratio inversion or cancellation between/within energy bins.

Conclusions

The proposed framework permits quantifying the spectral and spatial performance of PCDs under clinically realistic conditions at a given operating point. The presented example illustrates why PCDs should not be analyzed assuming that they are linear systems. The framework can, for example, be used to guide the development of PCDs and PCD-based systems. Furthermore, it can be applied to adapt commonly used measures, such as the modulation transfer function, to non-linear PCDs.

Monte Carlo simulations of microdosimetry and radiolytic species production at long time post proton irradiation using GATE and Geant4-DNA

BACKGROUND

Radiobiological effectiveness of radiation in cancer treatment can be studied at different scales (molecular till organ scale) and different time post irradiation. The production of free radicals and reactive oxygen species during water radiolysis is particularly relevant to understand the fundamental mechanisms playing a role in observed biological outcomes. The development and validation of Monte Carlo tools integrating the simulation of physical, physico-chemical and chemical stages after radiation is very important to maintain with experiments.

PURPOSE

Therefore, in this study, we propose to validate a new Geant4-DNA chemistry module through the simulation of water radiolysis and Fricke dosimetry experiments on a proton preclinical beam line.

MATERIAL AND METHODS

In this study, we used the GATE Monte Carlo simulation platform (version 9.3) to simulate a 67.5 MeV proton beam produced with the ARRONAX isochronous cyclotron (IBA Cyclone 70XP) at conventional dose rate (0.2 Gy/s) to simulate the irradiation of ultra-pure liquid water samples and Fricke dosimeter. We compared the depth dose profile with measurements performed with a plane parallel Advanced PTW 34045 Markus ionization chamber. Then, a new Geant4-DNA chemistry application proposed from Geant4 version 11.2 has been used to assess the evolution ofHO • ${\mathrm{HO}}^ \bullet $ ,e aq - ${\mathrm{e}}_{{\mathrm{aq}}}^ - $ ,H 3 O + ${{\mathrm{H}}}_3{{\mathrm{O}}}^ + $ ,H 2 O 2 ${{\mathrm{H}}}_2{{\mathrm{O}}}_2$ ,H 2 ${{\mathrm{H}}}_2$ ,HO 2 • ${\mathrm{HO}}_2^ \bullet $ ,HO 2 - , O 2 • - ${\mathrm{HO}}_2^ - ,{\mathrm{\ O}}_2^{ \bullet - }$ andHO - ${\mathrm{HO}}^ - $ reactive species along time until 1-h post-irradiation. In particular, the effect of oxygen and pH has been investigated through comparisons with experimental measurements of radiolytic yields forH 2 O 2 ${{\mathrm{H}}}_2{{\mathrm{O}}}_2$ and Fe3+.

RESULTS

GATE simulations reproduced, within 4%, the depth dose profile in liquid water. With Geant4-DNA, we were able to reproduce experimentalH 2 O 2 ${{\mathrm{H}}}_2{{\mathrm{O}}}_2$ radiolytic yields 1-h post-irradiation in aerated and deaerated conditions, showing the impact of small changes in oxygen concentrations on species evolution along time. For the Fricke dosimeter, simulated G(Fe3+) is 15.97 ± 0.2 molecules/100 eV which is 11% higher than the measured value (14.4 ± 04 molecules/100 eV).

CONCLUSIONS

These results aim to be consolidated by new comparisons involving other radiolytic species, such ase aq - ${\mathrm{e}}_{{\mathrm{aq}}}^ - $ or, O 2 • - $,{\mathrm{\ O}}_2^{ \bullet - }$ to further study the mechanisms underlying the FLASH effect observed at ultra-high dose rates (UHDR).

An International Study of Factors Affecting Variability of Dosimetry Calculations, Part 3: Contribution from Calculating Absorbed Dose from Time-Integrated Activity

Image-based dosimetry-guided radiopharmaceutical therapy has the potential to personalize treatment by limiting toxicity to organs at risk and maximizing the therapeutic effect. The 177Lu dosimetry challenge of the Society of Nuclear Medicine and Molecular Imaging consisted of 5 tasks assessing the variability in the dosimetry workflow. The fifth task investigated the variability associated with the last step, dose conversion, of the dosimetry workflow on which this study is based. Methods: Reference variability was assessed by 2 medical physicists using different software, methods, and all possible combinations of input segmentation formats and time points as provided in the challenge. General descriptive statistics for absorbed dose values from the global submissions from participants were calculated, and variability was measured using the quartile coefficient of dispersion. Results: For the liver, which included lesions with high uptake, variabilities of up to 36% were found. The baseline analysis showed a variability of 29% in absorbed dose results for the liver from datasets where lesions included and excluded were grouped, indicating that variation in how lesions in normal liver were treated was a significant source of variability. For other organs and lesions, variability was within 7%, independently of software used except for the local deposition method. Conclusion: The choice of dosimetry method or software had a small contribution to the overall variability of dose estimates.

DosePatch: physics-inspired cropping layout for patch-based Monte Carlo simulations to provide fast and accurate internal dosimetry

BACKGROUND

Dosimetry-based personalized therapy was shown to have clinical benefits e.g. in liver selective internal radiation therapy (SIRT). Yet, there is no consensus about its introduction into clinical practice, mainly as Monte Carlo simulations (gold standard for dosimetry) involve massive computation time. We addressed the problem of computation time and tested a patch-based approach for Monte Carlo simulations for internal dosimetry to improve parallelization. We introduce a physics-inspired cropping layout for patch-based MC dosimetry, and compare it to cropping layouts of the literature as well as dosimetry using organ-S-values, and dose kernels, taking whole-body Monte Carlo simulations as ground truth. This was evaluated in five patients receiving Yttrium-90 liver SIRT.

RESULTS

The patch-based Monte Carlo approach yielded the closest results to the ground truth, making it a valid alternative to the conventional approach. Our physics-inspired cropping layout and mosaicking scheme yielded a voxel-wise error of < 2% compared to whole-body Monte Carlo in soft tissue, while requiring only ≈  10% of the time.

CONCLUSIONS

This work demonstrates the feasibility and accuracy of physics-inspired cropping layouts for patch-based Monte Carlo simulations.

Characterizing the voxel-based approaches in radioembolization dosimetry with reDoseMC

BACKGROUND

Yttrium-90 (90 Y $^{90}{\rm {Y}}$ ) represents the primary radioisotope used in radioembolization procedures, while holmium-166 (166 Ho $^{166}{\rm {Ho}}$ ) is hypothesized to serve as a viable substitute for90 Y $^{90}{\rm {Y}}$ due to its comparable therapeutic potential and improved quantitative imaging. Voxel-based dosimetry for these radioisotopes relies on activity images obtained through PET or SPECT and dosimetry methods, including the voxel S-value (VSV) and the local deposition method (LDM). However, the evaluation of the accuracy of absorbed dose calculations has been limited by the use of non-ideal reference standards and investigations restricted to the liver. The objective of this study was to expand upon these dosimetry characterizations by investigating the impact of image resolutions, voxel sizes, target volumes, and tissue materials on the accuracy of90 Y $^{90}{\rm {Y}}$ and166 Ho $^{166}{\rm {Ho}}$ dosimetry techniques.

METHODS

A specialized radiopharmaceutical dosimetry software called reDoseMC was developed using the Geant4 Monte Carlo toolkit and validated by benchmarking the generated90 Y $^{90}{\rm {Y}}$ kernels with published data. The decay spectra of both90 Y $^{90}{\rm {Y}}$ and166 Ho $^{166}{\rm {Ho}}$ were also compared. Multiple VSV kernels were generated for the liver, lungs, soft tissue, and bone for isotropic voxel sizes of 1 mm, 2 mm, and 4 mm. Three theoretical phantom setups were created with 20 or 40 mm activity and mass density inserts for the same three voxel sizes. To replicate the limited spatial resolutions present in PET and SPECT images, image resolutions were modeled using a 3D Gaussian kernel with a Full Width at Half Maximum (FWHM) ranging from 0 to 16 mm and with no added noise. The VSV and LDM dosimetry methods were evaluated by characterizing their respective kernels and analyzing their absorbed dose estimates calculated on theoretical phantoms. The ground truth for these estimations was calculated using reDoseMC.

RESULTS

The decay spectra obtained through reDoseMC showed less than a 1% difference when compared to previously published experimental data for energies below 1.9 MeV in the case of90 Y $^{90}{\rm {Y}}$ and less than 1% for energies below 1.5 MeV for166 Ho $^{166}{\rm {Ho}}$ . Additionally, the validation kernels for90 Y $^{90}{\rm {Y}}$ VSV exhibited results similar to those found in published Monte Carlo codes, with source dose depositions having less than a 3% error margin. Resolution thresholds (FWHM thresh s ${\rm {FWHM}}_\mathrm{thresh}{\rm {s}}$ ), defined as resolutions that resulted in similar dose estimates between the LDM and VSV methods, were observed for90 Y $^{90}{\rm {Y}}$ . They were 1.5 mm for bone, 2.5 mm for soft tissue and liver, and 8.5 mm for lungs. For166 Ho $^{166}{\rm {Ho}}$ , the accuracy of absorbed dose deposition was found to be dependent on the contributions of absorbed dose from photons. Volume errors due to variations in voxel size impacted the final dose estimates. Larger target volumes yielded more accurate mean doses than smaller volumes. For both radioisotopes, the radial dose profiles for the VSV and LDM approximated but never matched the reference standard.

CONCLUSIONS

reDoseMC was developed and validated for radiopharmaceutical dosimetry. The accuracy of voxel-based dosimetry was found to vary widely with changes in image resolutions, voxel sizes, chosen target volumes, and tissue material; hence, the standardization of dosimetry protocols was found to be of great importance for comparable dosimetry analysis.

Active and passive dosimetry for beta-emitting radiopharmaceutical therapy agents in a custom SPECT/CT compatible phantom

Objective. This work introduces a novel approach to performing active and passive dosimetry for beta-emitting radionuclides in solution using common dosimeters. The measurements are compared to absorbed dose to water (Dw) estimates from Monte Carlo (MC) simulations. We present a method for obtaining absorbed dose to water, measured with dosimeters, from beta-emitting radiopharmaceutical agents using a custom SPECT/CT compatible phantom for validation of Monte Carlo based absorbed dose to water estimates.Approach. A cylindrical, acrylic SPECT/CT compatible phantom capable of housing an IBA EFD diode, Exradin A20-375 parallel plate ion chamber, unlaminated EBT3 film, and thin TLD100 microcubes was constructed for the purpose of measuring absorbed dose to water from solutions of common beta-emitting radiopharmaceutical therapy agents. The phantom is equipped with removable detector inserts that allow for multiple configurations and is designed to be used for validation of image-based absorbed dose estimates with detector measurements. Two experiments with131I and one experiment with177Lu were conducted over extended measurement intervals with starting activities of approximately 150-350 MBq. Measurement data was compared to Monte Carlo simulations using the egs_chamber user code in EGSnrc 2019.Main results. Agreement withink= 1 uncertainty between measured and MC predictedDwwas observed for all dosimeters, except the A20-375 ion chamber during the second131I experiment. Despite the agreement, the measured values were generally lower than predicted values by 5%-15%. The uncertainties atk = 1 remain large (5%-30% depending on the dosimeter) relative to other forms of radiation therapy.Significance. Despite high uncertainties, the overall agreement between measured and simulated absorbed doses is promising for the use of dosimeter-based RPT measurements in the validation of MC predictedDw.

GATE Monte Carlo approach to heterogeneity dose distribution in small fields used in radiation therapy

The Accurate dosage prediction in Radiation Therapy is challenging, prompting a need for precision beyond conventional clinical Treatment Planning Systems (TPS). Monte Carlo-based methods are sought for their superior accuracy. The aim of this study is to compare dose distributions between the ACUROS algorithm and the GATE platform in various tissue densities and field sizes, focusing on smaller fields. This study was initiated with a homogeneous validation of the TrueBeam STX system, using measurements obtained from the Centre Hospitalier Interregional Edith Cavell (CHIREC) in Brussels. The validation compared dosimetric functions (Percentage Depth Dose (PDD), Dose profile (DP) and Collimator scatter fraction (CSF)) employing the GAMMA index with a 2% / 2 mm criterion tolerance. Following this, heterogeneous studies examined dose distributions between the ACUROS algorithm and the GATE platform in various tissue densities and field sizes, with a specific focus on smaller fields. Simulations were conducted using both platforms on chest phantoms with heterogeneous slabs representing bone, lung, and heart, each housing a central tumor. The impact of electronic equilibrium on tumors for different small field sizes was evaluated. Results showed a remarkable 99% agreement between measurements and GATE calculations in the homogeneous validation of the TrueBeam STX system. However, in heterogeneous studies, ACUROS consistently overestimated lung doses by up to 8% compared to GATE simulation, especially evident with a flattening filter and smaller beam sizes at density interfaces. This highlights significant dose estimation discrepancies between ACUROS and GATE, emphasizing the need for precise calculations. The findings support exploring Monte Carlo-based methods for enhanced accuracy in Radiation Therapy treatment planning.

Edge illumination x-ray phase contrast simulations using the CAD-ASTRA toolbox

Edge illumination x-ray phase contrast imaging (XPCI) provides increased contrast for low absorbing materials compared to attenuation images and sheds light on the material microstructure through dark field contrast. To apply XPCI in areas such as non-destructive testing and inline inspection, where scanned samples are increasingly compared to simulated reference images, accurate and efficient simulation software is required. However, currently available simulators rely on expensive Monte Carlo techniques or wave-optics frameworks, resulting in long simulation times. Furthermore, these simulators are often not optimized to work with computer-aided design (CAD) models, a common and memory-efficient method to represent manufactured objects, hindering their integration in an inspection pipeline. In this work, we address these shortcomings by introducing an edge illumination XPCI simulation framework built upon the recently developed CAD-ASTRA toolbox. CAD-ASTRA allows for the efficient simulation of x-ray projections from CAD models through GPU-accelerated ray tracing and supports ray refraction in a geometric optics framework. The edge illumination implementation is validated and its performance is benchmarked against GATE, a state-of-the-art Monte Carlo simulator, revealing a simulation speed increase of up to three orders of magnitude, while maintaining high accuracy in the resulting images.

Linear Boltzmann equation solver for voxel-level dosimetry in radiopharmaceutical therapy: Comparison with Monte Carlo and kernel convolution

BACKGROUND

With recent interest in patient-specific dosimetry for radiopharmaceutical therapy (RPT) and selective internal radiation therapy (SIRT), an increasing number of voxel-based algorithms are being evaluated. Monte Carlo (MC) radiation transport, generally considered to be the most accurate among different methods for voxel-level absorbed dose estimation, can be computationally inefficient for routine clinical use.

PURPOSE

This work demonstrates a recently implemented grid-based linear Boltzmann transport equation (LBTE) solver for fast and accurate voxel-based dosimetry in RPT and SIRT and benchmarks it against MC.

METHODS

A deterministic LBTE solver (Acuros MRT) was implemented within a commercial RPT dosimetry package (Velocity 4.1). The LBTE is directly discretized using an adaptive mesh refined grid and then the coupled photon-electron radiation transport is iteratively solved inside specified volumes to estimate radiation doses from both photons and charged particles in heterogeneous media. To evaluate the performance of the LBTE solver for RPT and SIRT applications, 177 Lu SPECT/CT, 90 Y PET/CT, and 131 I SPECT/CT images of phantoms and patients were used. Multiple lesions (2-1052 mL) and normal organs were delineated for each study. Voxel dosimetry was performed with the LBTE solver, dose voxel kernel (DVK) convolution with density correction, and a validated in-house MC code using the same time-integrated activity and density maps as input to the different dose engines. The resulting dose maps, difference maps, and dose-volume-histogram (DVH) metrics were compared, to assess the voxel-level agreement. Evaluation of mean absorbed dose included comparison with structure-level estimates from OLINDA.

RESULTS

In the phantom inserts/compartments, the LBTE solver versus MC and DVK convolution demonstrated good agreement with mean absorbed dose and DVH metrics agreeing to within 5% except for the D90 and D70 metrics of a very low activity concentration insert of 90 Y where the agreement was within 15%. In the patient studies (five patients imaged after 177 Lu DOTATATE RPT, five after 90 Y SIRT, and two after 131 I radioimmunotherapy), in general, there was better agreement between the LBTE solver and MC than between LBTE solver and DVK convolution for mean absorbed dose and voxel-level evaluations. Across all patients for all three radionuclides, for soft tissue structures (kidney, liver, lesions), the mean absorbed dose estimates from the LBTE solver were in good agreement with those from MC (median difference < 1%, maximum 9%) and those from DVK (median difference < 5%, maximum 9%). The LBTE and OLINDA estimates for mean absorbed dose in kidneys and liver agreed to within 10%, but differences for lesions were larger with a maximum 14% for 177 Lu, 23% for 90 Y, and 26% for 131 I. For bone regions, the agreement in mean absorbed doses between LBTE and both MC and DVK were similar (median < 11%, max 11%) while for lung the agreement between LBTE and MC (median < 1%, max 8%) was substantially better than between LBTE and DVK (median < 16%, max 33%). Voxel level estimates for soft tissue structures also showed good agreement between the LBTE solver and both MC and DVK with a median difference < 5% (maximum < 13%) for the DVH metrics with all three radionuclides. The largest difference in DVH metrics was for the D90 and D70 metric in lung and bone where the uptake was low. Here, the difference between LBTE and MC had a median value < 14% (maximum 23%) for bone and < 4% (maximum 37%) for lung, while the corresponding differences between LBTE and DVK were < 23% (maximum 31%) and < 67% (maximum 313%), respectively. For a typical patient with a matrix size of 166 × 166 × 129 (voxel size 3 × 3 × 3 mm3 ), voxel dosimetry using the LBTE solver was as fast as ∼2 min on a desktop computer.

CONCLUSION

Having established good agreement between the LBTE solver and MC for RPT and SIRT applications, the LBTE solver is a viable option for voxel dosimetry that can be faster than MC. Further analysis is being performed to encompass the broad range of radionuclides and conditions encountered clinically.

Evaluation of monte carlo to support commissioning of the treatment planning system of new pencil beam scanning proton therapy facilities

Objective. To demonstrate the potential of Monte Carlo (MC) to support the resource-intensive measurements that comprise the commissioning of the treatment planning system (TPS) of new proton therapy facilities.Approach. Beam models of a pencil beam scanning system (Varian ProBeam) were developed in GATE (v8.2), Eclipse proton convolution superposition algorithm (v16.1, Varian Medical Systems) and RayStation MC (v12.0.100.0, RaySearch Laboratories), using the beam commissioning data. All models were first benchmarked against the same commissioning data and validated on seven spread-out Bragg peak (SOBP) plans. Then, we explored the use of MC to optimise dose calculation parameters, fully understand the performance and limitations of TPS in homogeneous fields and support the development of patient-specific quality assurance (PSQA) processes. We compared the dose calculations of the TPSs against measurements (DDTPSvs.Meas.) or GATE (DDTPSvs.GATE) for an extensive set of plans of varying complexity. This included homogeneous plans with varying field-size, range, width, and range-shifters (RSs) (n= 46) and PSQA plans for different anatomical sites (n= 11).Main results. The three beam models showed good agreement against the commissioning data, and dose differences of 3.5% and 5% were found for SOBP plans without and with RSs, respectively. DDTPSvs.Meas.and DDTPSvs.GATEwere correlated in most scenarios. In homogeneous fields the Pearson's correlation coefficient was 0.92 and 0.68 for Eclipse and RayStation, respectively. The standard deviation of the differences between GATE and measurements (±0.5% for homogeneous and ±0.8% for PSQA plans) was applied as tolerance when comparing TPSs with GATE. 72% and 60% of the plans were within the GATE predicted dose difference for both TPSs, for homogeneous and PSQA cases, respectively.Significance. Developing and validating a MC beam model early on into the commissioning of new proton therapy facilities can support the validation of the TPS and facilitate comprehensive investigation of its capabilities and limitations.

Technical note: Errors introduced when using Dose Voxel Kernels for estimating absorbed dose from radiopharmaceutical therapies involving alpha emitters

BACKGROUND

In radiopharmaceutical therapies (RPT) involving beta emitters, absorbed dose (Dabs ) calculations often employ the use of dose voxel kernels (DVK). Such methods are faster and easier to implement than Monte Carlo (MC) simulations. Using DVK methods implies a non-stochastic distribution of particles. This is a valid assumption for betas where thousands to tens of thousands of particles traversing the cell nucleus are required to achieve cell kill. However, alpha particles have linear energy transfers (LET) that are ∼500 times higher than LETs of betas. This results in a significant probability of killing a cell from even a single traversal through its nucleus. Consequently, the activity used for therapy involving alphas is very low, and the use of DVKs for estimating Dabs will generate results that may be erroneous.

PURPOSE

This work aims at illustrating how use of DVKs affect the resulting Dabs in small tumors when irradiated with clinically relevant amounts of beta- and alpha-emitters. The results are compared with those from using a Monte Carlo method where the energy deposition from individual tracks is simulated.

METHODS

To illustrate the issues associated with DVK for alpha radiopharmaceutical therapies at the microscale, a tumor cluster model was used to compare beta (177 Lu) and alphas (211 At, 225 Ac, and 227 Th) irradiations. We used 103 beta particles and 20 alpha particles per cell, which is within the range of the required number of particle traversals through its nucleus to sterilize a cell. Results from using both methods were presented with Dabs histograms, dose volume histograms, and Dabs error maps.

RESULTS

For beta-emitter (177 Lu) irradiating the modeled tumor cluster, resulting Dabs was similar for both DVK and MC methods. For all alpha emitters, the use of DVK led to an overestimation of Dabs when compared to results generated using a MC approach.

CONCLUSIONS

Our results demonstrate that the use of DVK methods for alpha emitters can lead to an overestimation in the calculated Dabs . The use of DVKs for therapies involving alpha emitters may therefore not be appropriate when only referring to the mean Dabs metric.

Automatic planning for head and neck seed implant brachytherapy based on deep convolutional neural network dose engine

BACKGROUND

Seed implant brachytherapy (SIBT) is an effective treatment modality for head and neck (H&N) cancers; however, current clinical planning requires manual setting of needle paths and utilizes inaccurate dose calculation algorithms.

PURPOSE

This study aims to develop an accurate and efficient deep convolutional neural network dose engine (DCNN-DE) and an automatic SIBT planning method for H&N SIBT.

METHODS

A cohort of 25 H&N patients who received SIBT was utilized to develop and validate the methods. The DCNN-DE was developed based on 3D-unet model. It takes single seed dose distribution from a modified TG-43 method, the CT image and a novel inter-seed shadow map (ISSM) as inputs, and predicts the dose map of accuracy close to the one from Monte Carlo simulations (MCS). The ISSM was proposed to better handle inter-seed attenuation. The accuracy and efficacy of the DCNN-DE were validated by comparing with other methods taking MCS dose as reference. For SIBT planning, a novel strategy inspired by clinical practice was proposed to automatically generate parallel or non-parallel potential needle paths that avoid puncturing bone and critical organs. A heuristic-based optimization method was developed to optimize the seed positions to meet clinical prescription requirements. The proposed planning method was validated by re-planning the 25 cases and comparing with clinical plans.

RESULTS

The absolute percentage error in the TG-43 calculation for CTV V100 and D90 was reduced from 5.4% and 13.2% to 0.4% and 1.1% with DCNN-DE, an accuracy improvement of 93% and 92%, respectively. The proposed planning method could automatically obtain a plan in 2.5 ± 1.5 min. The generated plans were judged clinically acceptable with dose distribution comparable with those of the clinical plans.

CONCLUSIONS

The proposed method can generate clinically acceptable plans quickly with high accuracy in dose evaluation, and thus has a high potential for clinical use in SIBT.

On the Use of Artificial Intelligence for Dosimetry of Radiopharmaceutical Therapies

Routine clinical dosimetry along with radiopharmaceutical therapies is key for future treatment personalization. However, dosimetry is considered complex and time-consuming with various challenges amongst the required steps within the dosimetry workflow. The general workflow for image-based dosimetry consists of quantitative imaging, the segmentation of organs and tumors, fitting of the time-activity-curves, and the conversion to absorbed dose. This work reviews the potential and advantages of the use of artificial intelligence to improve speed and accuracy of every single step of the dosimetry workflow.

Deep learning-based fast denoising of Monte Carlo dose calculation in carbon ion radiotherapy

BACKGROUND

Plan verification is one of the important steps of quality assurance (QA) in carbon ion radiotherapy. Conventional methods of plan verification are based on phantom measurement, which is labor-intensive and time-consuming. Although the plan verification method based on Monte Carlo (MC) simulation provides a more accurate modeling of the physics, it is also time-consuming when simulating with a large number of particles. Therefore, how to ensure the accuracy of simulation results while reducing simulation time is the current difficulty and focus.

PURPOSE

The purpose of this work was to evaluate the feasibility of using deep learning-based MC denoising method to accelerate carbon-ion radiotherapy plan verification.

METHODS

Three models, including CycleGAN, 3DUNet and GhostUNet with Ghost module, were used to denoise the 1 × 106 carbon ions-based MC dose distribution to the accuracy of 1 × 108 carbon ions-based dose distribution. The CycleGAN's generator, 3DUNet and GhostUNet were all derived from the 3DUNet network. A total of 59 cases including 29 patients with head-and-neck cancers and 30 patients with lung cancers were collected, and 48 cases were randomly selected as the training set of the CycleGAN network and six cases as the test set. For the 3DUNet and GhostUNet models, the numbers of training set, validation set, and test set were 47, 6, and 6, respectively. Finally, the three models were evaluated qualitatively and quantitatively using RMSE and three-dimensional gamma analysis (3 mm, 3%).

RESULTS

The three end-to-end trained models could be used for denoising the 1 × 106 carbon ions-based dose distribution, and their generalization was proved. The GhostUNet obtained the lowest RMSE value of 0.075, indicating the smallest difference between its denoised and 1 × 108 carbon ions-based dose distributions. The average gamma passing rate (GPR) between the GhostUNet denoising-based versus 1 × 108 carbon ions-based dose distributions was 99.1%, higher than that of the CycleGAN at 94.3% and the 3DUNet at 96.2%. Among the three models, the GhostUNet model had the fewest parameters (4.27 million) and the shortest training time (99 s per epoch) but achieved the best denoising results.

CONCLUSION

The end-to-end deep network GhostUNet outperforms the CycleGAN, 3DUNet models in denoising MC dose distributions for carbon ion radiotherapy. The network requires less than 5 s to denoise a sample of MC simulation with few particles to obtain a qualitative and quantitative result comparable to the dose distribution simulated by MC with relatively large number particles, offering a significant reduction in computation time.

Small field proton irradiation for in vivo studies: Potential and limitations when adapting clinical infrastructure

PURPOSE

To evaluate the dosimetric accuracy for small field proton irradiation relevant for pre-clinical in vivo studies using clinical infrastructure and technology. In this context additional beam collimation and range reduction was implemented.

METHODS AND MATERIALS

The clinical proton beam line employing pencil beam scanning (PBS) was adapted for the irradiation of small fields at shallow depths. Cylindrical collimators with apertures of 15, 12, 7 and 5mm as well as two different range shifter types, placed at different distances relative to the target, were tested: a bolus range shifter (BRS) attached to the collimator and a clinical nozzle mounted range shifter (CRS) placed at a distance of 72cm from the collimator. The Monte Carlo (MC) based dose calculation engine implemented in the clinical treatment planning system (TPS) was commissioned for these two additional hardware components. The study was conducted with a phantom and cylindrical target sizes between 2 and 25mm in diameter following a dosimetric end-to-end test concept.

RESULTS

The setup with the CRS provided a uniform dose distribution across the target. An agreement of better than5% between the planned dose and the measurements was obtained for a target with 3mm diameter (collimator 5mm). A 2mm difference between the collimator and the target diameter (target being 2 mm smaller than the collimator) sufficed to cover the whole target with the planned dose in the setup with CRS. Using the BRS setup (target 8mm, collimator 12mm) resulted in non-homogeneous dose distributions, with a dose discrepancy of up to 10% between the planned and measured doses.

CONCLUSION

The clinical proton infrastructure with adequate beam line adaptations and a state-of-the-art TPS based on MC dose calculations enables small animal irradiations with a high dosimetric precision and accuracy for target sizes down to 3mm.

A radiomic- and dosiomic-based machine learning regression model for pretreatment planning in 177 Lu-DOTATATE therapy

BACKGROUND

Standardized patient-specific pretreatment dosimetry planning is mandatory in the modern era of nuclear molecular radiotherapy, which may eventually lead to improvements in the final therapeutic outcome. Only a comprehensive definition of a dosage therapeutic window encompassing the range of absorbed doses, that is, helpful without being detrimental can lead to therapy individualization and improved outcomes. As a result, setting absorbed dose safety limits for organs at risk (OARs) requires knowledge of the absorbed dose-effect relationship. Data sets of consistent and reliable inter-center dosimetry findings are required to characterize this relationship.

PURPOSE

We developed and standardized a new pretreatment planning model consisting of a predictive dosimetry procedure for OARs in patients with neuroendocrine tumors (NETs) treated with 177 Lu-DOTATATE (Lutathera). In the retrospective study described herein, we used machine learning (ML) regression algorithms to predict absorbed doses in OARs by exploiting a combination of radiomic and dosiomic features extracted from patients' imaging data.

METHODS

Pretreatment and posttreatment data for 20 patients with NETs treated with 177 Lu-DOTATATE were collected from two clinical centers. A total of 3412 radiomic and dosiomic features were extracted from the patients' computed tomography (CT) scans and dose maps, respectively. All dose maps were generated using Monte Carlo simulations. An ML regression model was designed based on ML algorithms for predicting the absorbed dose in every OAR (liver, left kidney, right kidney, and spleen) before and after the therapy and between each therapy session, thus predicting any possible radiotoxic effects.

RESULTS

We evaluated nine ML regression algorithms. Our predictive model achieved a mean absolute dose error (MAE, in Gy) of 0.61 for the liver, 1.58 for the spleen, 1.30 for the left kidney, and 1.35 for the right kidney between pretherapy 68 Ga-DOTATOC positron emission tomography (PET)/CT and posttherapy 177 Lu-DOTATATE single photon emission (SPECT)/CT scans. Τhe best predictive performance observed was based on the gradient boost for the liver, the left kidney and the right kidney, and on the extra tree regressor for the spleen. Evaluation of the model's performance according to its ability to predict the absorbed dose in each OAR in every possible combination of pretherapy 68 Ga-DOTATOC PET/CT and any posttherapy 177 Lu-DOTATATE treatment cycle SPECT/CT scans as well as any 177 Lu-DOTATATE SPECT/CT treatment cycle and the consequent 177 Lu-DOTATATE SPECT/CT treatment cycle revealed mean absorbed dose differences ranges from -0.55 to 0.68 Gy. Incorporating radiodosiomics features from the 68 Ga-DOTATOC PET/CT and first 177 Lu-DOTATATE SPECT/CT treatment cycle scans further improved the precision and minimized the standard deviation of the predictions in nine out of 12 instances. An average improvement of 57.34% was observed (range: 17.53%-96.12%). However, it's important to note that in three instances (i.e., Ga,C.1 → C3 in spleen and left kidney, and Ga,C.1 → C2 in right kidney) we did not observe an improvement (absolute differences of 0.17, 0.08, and 0.05 Gy, respectively). Wavelet-based features proved to have high correlated predictive value, whereas non-linear-based ML regression algorithms proved to be more capable than the linear-based of producing precise prediction in our case.

CONCLUSIONS

The combination of radiomics and dosiomics has potential utility for personalized molecular radiotherapy (PMR) response evaluation and OAR dose prediction. These radiodosiomic features can potentially provide information on any possible disease recurrence and may be highly useful in clinical decision-making, especially regarding dose escalation issues.

Pre-treatment dosimetry in90Y-SIRT: Is it possible to optimise SPECT reconstruction parameters and calculation methods for accurate dosimetry?

PURPOSE

The aim of this study was (a) to optimise the99mTc-SPECT reconstruction parameters for the pre-treatment dosimetry of90Y-selective internal radiation therapy (SIRT) and (b) to compare the accuracy of clinical dosimetry methods with full Monte-Carlo dosimetry (fMCD) performed with Gate.

METHODS

To optimise the reconstruction parameters, two hundred reconstructions with different parameters were performed on a NEMA phantom, varying the number of iterations, subsets, and post-filtering. The accuracy of the dosimetric methods was then investigated using an anthropomorphic phantom. Absorbed dose maps were generated using (1) the Partition Model (PM), (2) the Dose Voxel Kernel (DVK) convolution, and (3) the Local Deposition Method (LDM) with known activity restricted to the whole phantom (WP) or to the liver and lungs (LL). The dose to the lungs was calculated using the "multiple DVK" and "multiple LDM" methods.

RESULTS

Optimal OSEM reconstruction parameters were found to depend on object size and dosimetric criterion chosen (Dmean or DVH-derived metric). The Dmean of all three dosimetric methods was close (≤ 10%) to the Dmean of fMCD simulations when considering large segmented volumes (whole liver, normal liver). In contrast, the Dmean to the small volume (∅=31) was systemically underestimated (12%-25%). For lungs, the "multiple DVK" and "multiple LDM" methods yielded a Dmean within 20% for the WP method and within 10% for the LL method.

CONCLUSIONS

All three methods showed a substantial degradation of the dose-volume histograms (DVHs) compared to fMCD simulations. The DVK and LDM methods performed almost equally well, with the "multiple DVK" method being more accurate in the lungs.

Monocrystalline diamond detector for online monitoring during synchrotron microbeam radiotherapy

Microbeam radiation therapy (MRT) is a radiotherapy technique combining spatial fractionation of the dose distribution on a micrometric scale, X-rays in the 50-500 keV range and dose rates up to 16 × 103 Gy s-1. Nowadays, in vivo dosimetry remains a challenge due to the ultra-high radiation fluxes involved and the need for high-spatial-resolution detectors. The aim here was to develop a striped diamond portal detector enabling online microbeam monitoring during synchrotron MRT treatments. The detector, a 550 µm bulk monocrystalline diamond, is an eight-strip device, of height 3 mm, width 178 µm and with 60 µm spaced strips, surrounded by a guard ring. An eight-channel ASIC circuit for charge integration and digitization has been designed and tested. Characterization tests were performed at the ID17 biomedical beamline of the European Synchrotron Radiation Facility (ESRF). The detector measured direct and attenuated microbeams as well as interbeam fluxes with a precision level of 1%. Tests on phantoms (RW3 and anthropomorphic head phantoms) were performed and compared with simulations. Synchrotron radiation measurements were performed on an RW3 phantom for strips facing a microbeam and for strips facing an interbeam area. A 2% difference between experiments and simulations was found. In more complex geometries, a preliminary study showed that the absolute differences between simulated and recorded transmitted beams were within 2%. Obtained results showed the feasibility of performing MRT portal monitoring using a microstriped diamond detector. Online dosimetric measurements are currently ongoing during clinical veterinary trials at ESRF, and the next 153-strip detector prototype, covering the entire irradiation field, is being finalized at our institution.

Dosimetric Analysis of Rhizophora-based Phantom Material in Radiation Therapy Applications Using Monte Carlo GATE Simulation

Purpose

This study aims to determine the percentage depth dose (PDD) of a phantom material made from soy-lignin bonded Rhizophora spp. particleboard coated with a gloss finish by using Monte Carlo Geant4 Application for Tomographic Emission (GATE) simulation.

Materials and Methods

The particleboard was fabricated using a hot pressing technique at target density of 1.0 g·cm-3 and the elemental fraction was recorded for the simulation. The PDD was simulated in the GATE simulation using the linear accelerator Elekta Synergy model for the water phantom and Rhizophora phantom, and the results were compared with the experimental PDD performed by several studies. Beam flatness and beam symmetry were also measured in this study.

Results

The simulated PDD for Rhizophora and water was in agreement with the experimental PDD of water with overall discrepancies of 0% to 8.7% at depth ranging from 1.0 to 15.0 cm. In the GATE simulation, all the points passed the clinical 3%/3 mm criterion in comparison with water, with the final percentage of 2.34% for Rhizophora phantom and 2.49% for the water phantom simulated in GATE. Both the symmetries are all within the range of an acceptable value of 2.0% according to the recommendation, with the beam symmetry of the water phantom and Rhizophora phantom at 0.58% and 0.28%, respectively.

Conclusions

The findings of this study provide the necessary foundation to confidently use the phantom for radiotherapy purposes, especially in treatment planning.

Tumor Control Probability and Small-Scale Monte Carlo Dosimetry: Effects of Heterogenous Intratumoral Activity Distribution in Radiopharmaceutical Therapy

In radiopharmaceutical therapy, intratumoral uptake of radioactivity usually leads to heterogeneous absorbed dose distribution. The likelihood of treatment success can be estimated with the tumor control probability (TCP), which requires accurate dosimetry, estimating the absorbed dose rate per unit activity to individual tumor cells. Methods: Xenograft cryosections of the prostate cancer cell line LNCaP treated with [177Lu]Lu-PSMA-617 were evaluated with digital autoradiography and stained with hematoxylin and eosin. The digital autoradiography images were used to define the source in a Monte Carlo simulation of the absorbed dose, and the stained sections were used to detect the position of cell nuclei to relate the intratumoral absorbed dose heterogeneity to the cell density. Simulations were performed for 225Ac, 177Lu, and 90Y. TCP was calculated to estimate the mean necessary injected activity for a high TCP. A hypothetical case of activity mainly taken up on the tumor borders was generated and used to simulate the absorbed dose. Results: The absorbed dose per decay to tumor cells was calculated from the staining and simulation results to avoid underestimating the tumor response from low absorbed doses in tumor regions with low cell density. The mean of necessary injected activity to reach a 90% TCP for 225Ac, 177Lu, and 90Y was found to be 18.3 kBq (range, 18-22 kBq), 24.3 MBq (range, 20-29 MBq), and 5.6 MBq (range, 5-6 MBq), respectively. Conclusion: To account for the heterogeneous absorbed dose generated from nonuniform intratumoral activity uptake, dosimetry models can estimate the mean necessary activity to reach a sufficient TCP for treatment response. This approach is necessary to accurately evaluate the efficacy of suggested radiopharmaceuticals for therapy.

Optimization of the Eclipse TPS beam configuration parameters for small field dosimetry using Monte Carlo simulations and experimental measurements

PURPOSE

To evaluate the impact of tuning the beam configurations parameters on the Analytical Anisotropic Algorithm (AAA) and the Acuros XB (AXB) algorithm for small fields using Monte Carlo simulations and measurements.

METHODS

The TrueBeam STx with the high-definition 120 multi-leaf collimator (HD120-MLC) was modeled with Geant4 application for emission tomography (GATE) Monte Carlo platform and validated against measurements. The impact of varying the effective spot size (ESS) and dosimetric leaf gap (DLG) on AAA and AXB calculations was carried out for small MLC-fields ranging from 0.5×0.5 cm2 to 3 × 3 cm2. Beam penumbras, field sizes and output factors calculated by AAA and AXB were compared to GATE calculations and measurements.

RESULTS

The beam penumbra comparisons showed that the best ESS value for AXB was about 1.0 mm in the crossplane direction and 0.5 mm in the inplane direction. By optimizing the ESS values, AXB could provide output factor results almost within 2% of GATE calculations and measurements for fields down to 0.5×0.5 cm2. For AAA, significant output factor differences were observed for all ESS values and tuning the DLG in addition to the ESS optimization resulted in an absorbed dose difference of less than 2.5% for MLC-fields down to 1 × 1 cm2.

CONCLUSION

By optimizing the ESS values, AXB can achieve accurate output factors in the case of small MLC-fields without the need of DLG tuning. Nevertheless, compromises between the output factor, DLG and ESS values were found necessary for AAA calculations. A MLC model improvement would allow to avoid the complexity related to tuning the configuration parameters.

First evidence of in vivo effect of FLASH radiotherapy with helium ions in zebrafish embryos

The ability to reduce toxicity of ultra-high dose rate (UHDR) helium ion irradiation has not been reported in vivo. Here, we tested UHDR helium ion irradiation in an embryonic zebrafish model. Our results show that UHDR helium ions spare body development and reduce spine curvature, compared to conventional dose rate.

Normal organ dosimetry for thyroid cancer patients treated with radioiodine as part of the multi-centre multi-national Horizon 2020 MEDIRAD project

PURPOSE

Dosimetry is rarely performed for the treatment of differentiated thyroid cancer patients with Na[131I]I (radioiodine), and information regarding absorbed doses delivered is limited. Collection of dosimetry data in a multi-centre setting requires standardised quantitative imaging and dosimetry. A multi-national, multi-centre clinical study was performed to assess absorbed doses delivered to normal organs for differentiated thyroid cancer patients treated with Na[131I]I.

METHODS

Patients were enrolled in four centres and administered fixed activities of 1.1 or 3.7 GBq of Na[131I]I using rhTSH stimulation or under thyroid hormone withdrawal according to local protocols. Patients were imaged using SPECT(/CT) at variable imaging time-points following standardised acquisition and reconstruction protocols. Whole-body retention data were collected. Dosimetry for normal organs was performed at two dosimetry centres and results collated.

RESULTS

One hundred and five patients were recruited. Median absorbed doses per unit administered activity of 0.44, 0.14, 0.05 and 0.16 mGy/MBq were determined for the salivary glands of patients treated at centre 1, 2, 3 and 4, respectively. Median whole-body absorbed doses for 1.1 and 3.7 GBq were 0.05 Gy and 0.16 Gy, respectively. Median whole-body absorbed doses per unit administered activity of 0.04, 0.05, 0.04 and 0.04 mGy/MBq were calculated for centre 1, 2, 3 and 4, respectively.

CONCLUSIONS

A wide range of normal organ doses were observed for differentiated thyroid cancer patients treated with Na[131I]I, highlighting the necessity for individualised dosimetry. The results show that data may be collated from multiple centres if minimum standards for the acquisition and dosimetry protocols can be achieved.

Radiation-induced double-strand breaks by internal ex vivo irradiation of lymphocytes: Validation of a Monte Carlo simulation model using GATE and Geant4-DNA

This study describes a method to validate a radiation transport model that quantifies the number of DNA double-strand breaks (DSB) produced in the lymphocyte nucleus by internal ex vivo irradiation of whole blood with the radionuclides 90Y, 99mTc, 123I, 131I, 177Lu, 223Ra, and 225Ac in a test vial using the GATE/Geant4 code at the macroscopic level and the Geant4-DNA code at the microscopic level.

METHODS

The simulation at the macroscopic level reproduces an 8 mL cylindrical water-equivalent medium contained in a vial that mimics the geometry for internal ex vivo blood irradiation. The lymphocytes were simulated as spheres of 3.75 µm radius randomly distributed, with a concentration of 125 spheres/mL. A phase-space actor was attached to each sphere to register all the entering particles. The simulation at the microscopic level for each radionuclide was performed using the Geant4-DNA tool kit, which includes the clustering example centered on a density-based spatial clustering of applications with noise (DBSCAN) algorithm. The irradiation source was constructed by generating a single phase space from the sum of all phase spaces. The lymphocyte nucleus was defined as a water sphere of a 3.1 µm radius. The absorbed dose coefficients for lymphocyte nuclei (dLymph) were calculated and compared with macroscopic whole blood absorbed dose coefficients (dBlood). The DBSCAN algorithm was used to calculate the number of DSBs. Lastly, the number of DSB∙cell-1∙mGy-1 (simulation) was compared with the number of radiation-induced foci per cell and absorbed dose (RIF∙cell-1∙mGy-1) provided by experimental data for gamma and beta emitting radionuclides. For alpha emitters, dLymph and the number of α-tracks∙100 cell-1∙mGy-1 and DBSs∙µm-1 were calculated using experiment-based thresholds for the α-track lengths and DBSs/track values. The results were compared with the results of an ex vivo study with 223Ra.

RESULTS

The dLymph values differed from the dBlood values by -1.0% (90Y), -5.2% (99mTc), -22.3% (123I), 0.35% (131I), 2.4% (177Lu), -5.6% (223Ra) and -6.1% (225Ac). The number of DSB∙cell-1∙mGy-1 for each radionuclide was 0.015 DSB∙cell-1∙mGy-1 (90Y), 0.012 DSB∙cell-1∙mGy-1 (99mTc), 0.014DSB∙cell-1∙mGy-1 (123I), 0.012 DSB∙cell-1∙mGy-1 (131I), and 0.016 DSB∙cell-1∙mGy-1 (177Lu). These values agree very well with experimental data. The number of α-tracks∙100 cells-1∙mGy-1 for 223Ra and 225Ac where 0.144 α-tracks∙100 cells-1∙mGy-1 and 0.151 α-tracks∙100 cells-1∙mGy-1, respectively. These values agree very well with experimental data. Moreover, the linear density of DSBs per micrometer α-track length were 11.13 ± 0.04 DSB/µm and 10.86 ± 0.06 DSB/µm for 223Ra and 225Ac, respectively.

CONCLUSION

This study describes a model to simulate the DNA DSB damage in lymphocyte nuclei validated by experimental data obtained from internal ex vivo blood irradiation with radionuclides frequently used in diagnostic and therapeutic procedures in nuclear medicine.

Updating 90Y Voxel S-Values including internal Bremsstrahlung: Monte Carlo study and development of an analytical model

PURPOSE

Internal Bremsstrahlung (IB) is a process accompanying β-decay but neglected in Voxel S-Values (VSVs) calculation. Aims of this work were to calculate, through Monte Carlo (MC) simulation, updated 90Y-VSVs including IB, and to develop an analytical model to evaluate 90Y-VSVs for any voxel size of practical interest.

METHODS

GATE (Geant4 Application for Tomographic Emission) was employed for simulating voxelized geometries of soft tissue, with voxels sides l ranging from 2 to 6 mm, in steps of 0.5 mm. The central voxel was set as a homogeneous source of 90Y when IB photons are not modelled. For each l, the VSVs were computed for 90Y decays alone and for 90Y + IB. The analytical model was then built through fitting procedures of the VSVs including IB contribution.

RESULTS

Comparing GATE-VSVs with and without IB, differences between + 25% and + 30% were found for distances from the central voxel larger than the maximum β-range. The analytical model showed an agreement with MC simulations within ± 5% in the central voxel and in the Bremsstrahlung tails, for any l value examined, and relative differences lower than ± 40%, for other distances from the source.

CONCLUSIONS

The presented 90Y-VSVs include for the first time the contribution due to IB, thus providing a more accurate set of dosimetric factors for three-dimensional internal dosimetry of 90Y-labelled radiopharmaceuticals and medical devices. Furthermore, the analytical model constitutes an easy and fast alternative approach for 90Y-VSVs estimation for non-standard voxel dimensions.

A GATE simulation study for dosimetry in cancer cell and micrometastasis from the 225Ac decay chain

BACKGROUND

Radiopharmaceutical therapy (RPT) with alpha-emitting radionuclides has shown great promise in treating metastatic cancers. The successive emission of four alpha particles in the ²²⁵Ac decay chain leads to highly targeted and effective cancer cell death. Quantifying cellular dosimetry for ²²⁵Ac RPT is essential for predicting cell survival and therapeutic success. However, the leading assumption that all ²²⁵Ac progeny remain localized at their target sites likely overestimates the absorbed dose to cancer cells. To address limitations in existing semi-analytic approaches, this work evaluates S-values for ²²⁵Ac's progeny radionuclides with GATE Monte Carlo simulations.

METHODS

The cellular geometries considered were an individual cell (10 µm diameter with a nucleus of 8 µm diameter) and a cluster of cells (micrometastasis) with radionuclides localized in four subcellular regions: cell membrane, cytoplasm, nucleus, or whole cell. The absorbed dose to the cell nucleus was scored, and self- and cross-dose S-values were derived. We also evaluated the total absorbed dose with various degrees of radiopharmaceutical internalization and retention of the progeny radionuclides ²²¹Fr (t_1/2 = 4.80 m) and ²¹³Bi (t_1/2 = 45.6 m).

RESULTS

For the cumulative ²²⁵Ac decay chain, our self- and cross-dose nuclear S-values were both in good agreement with S-values published by MIRDcell, with per cent differences ranging from - 2.7 to - 8.7% for the various radionuclide source locations. Source location had greater effects on self-dose S-values than the intercellular cross-dose S-values. Cumulative ²²⁵Ac decay chain self-dose S-values increased from 0.167 to 0.364 GyBq^-1 s^-1 with radionuclide internalization from the cell surface into the cell. When progeny migration from the target site was modelled, the cumulative self-dose S-values to the cell nucleus decreased by up to 71% and 21% for ²²¹Fr and ²¹³Bi retention, respectively.

CONCLUSIONS

Our GATE Monte Carlo simulations resulted in cellular S-values in agreement with existing MIRD S-values for the alpha-emitting radionuclides in the ²²⁵Ac decay chain. To obtain accurate absorbed dose estimates in ²²⁵Ac studies, accurate understanding of daughter migration is critical for optimized injected activities. Future work will investigate other novel preclinical alpha-emitting radionuclides to evaluate therapeutic potency and explore realistic cellular geometries corresponding to targeted cancer cell lines.

Fast dose calculation in x-ray guided interventions by using deep learning

Objective.Patient dose estimation in x-ray-guided interventions is essential to prevent radiation-induced biological side effects. Current dose monitoring systems estimate the skin dose based in dose metrics such as the reference air kerma. However, these approximations do not take into account the exact patient morphology and organs composition. Furthermore, accurate organ dose estimation has not been proposed for these procedures. Monte Carlo simulation can accurately estimate the dose by recreating the irradiation process generated during the x-ray imaging, but at a high computation time, limiting an intra-operative application. This work presents a fast deep convolutional neural network trained with MC simulations for patient dose estimation during x-ray-guided interventions.Approach.We introduced a modified 3D U-Net that utilizes a patient's CT scan and the numerical values of imaging settings as input to produce a Monte Carlo dose map. To create a dataset of dose maps, we simulated the x-ray irradiation process for the abdominal region using a publicly available dataset of 82 patient CT scans. The simulation involved varying the angulation, position, and tube voltage of the x-ray source for each scan. We additionally conducted a clinical study during endovascular abdominal aortic repairs to validate the reliability of our Monte Carlo simulation dose maps. Dose measurements were taken at four specific anatomical points on the skin and compared to the corresponding simulated doses. The proposed network was trained using a 4-fold cross-validation approach with 65 patients, and evaluating the performance on the remaining 17 patients during testing.Main results.The clinical validation demonstrated a average error within the anatomical points of 5.1%. The network yielded test errors of 11.5 ± 4.6% and 6.2 ± 1.5% for peak and average skin doses, respectively. Furthermore, the mean errors for the abdominal region and pancreas doses were 5.0 ± 1.4% and 13.1 ± 2.7%, respectively.Significance.Our network can accurately predict a personalized 3D dose map considering the current imaging settings. A short computation time was achieved, making our approach a potential solution for dose monitoring and reporting commercial systems.

Influence of the background in Compton camera images for proton therapy treatment monitoring

Objective. Background events are one of the most relevant contributions to image degradation in Compton camera imaging for hadron therapy treatment monitoring. A study of the background and its contribution to image degradation is important to define future strategies to reduce the background in the system.Approach. In this simulation study, the percentage of different kinds of events and their contribution to the reconstructed image in a two-layer Compton camera have been evaluated. To this end, GATE v8.2 simulations of a proton beam impinging on a PMMA phantom have been carried out, for different proton beam energies and at different beam intensities.Main results. For a simulated Compton camera made of Lanthanum (III) Bromide monolithic crystals, coincidences caused by neutrons arriving from the phantom are the most common type of background produced by secondary radiations in the Compton camera, causing between 13% and 33% of the detected coincidences, depending on the beam energy. Results also show that random coincidences are a significant cause of image degradation at high beam intensities, and their influence in the reconstructed images is studied for values of the time coincidence windows from 500 ps to 100 ns.Significance. Results indicate the timing capabilities required to retrieve the fall-off position with good precision. Still, the noise observed in the image when no randoms are considered make us consider further background rejection methods.

Investigation of TG-43 Dosimetric Parameters for 192Ir Brachytherapy Source Using GATE Monte Carlo Code

Purpose

According to the revised Task Group number 43 recommendations, a brachytherapy source must be validated against a similar or identical source before its clinical application. The purpose of this investigation is to verify the dosimetric data of the high dose rate (HDR) BEBIG 192Ir source (Ir2.A85-2).

Materials and Methods

The HDR 192Ir encapsulated seed was simulated and its main dosimetric data were calculated using Geant4 Application for Tomographic Emission (GATE) simulation code. Cubic cells were used for the calculation of dose rate constant and radial dose function while for anisotropy function ring cells were used. DoseActors were simulated and attached to the respective cells to obtain the required data.

Results

The dose rate constant was obtained as 1.098 ± 0.003 cGy.h - 1.U - 1, differing by 1.0% from the reference value reported by Granero et al. Similarly, the calculated values for radial dose and anisotropy functions presented good agreement with the results obtained by Granero et al.

Conclusion

The results of this study suggest that the GATE Monte Carlo code is a valid toolkit for benchmarking brachytherapy sources and can be used for brachytherapy simulation-based studies and verification of brachytherapy treatment planning systems.

Optimized SPECT Imaging of 224Ra α-Particle Therapy by 212Pb Photon Emissions

In preparation for an α-particle therapy trial using 1-7 MBq of ²²⁴Ra, the feasibility of tomographic SPECT/CT imaging was of interest. The nuclide decays in 6 steps to stable ²⁰⁸Pb, with ²¹²Pb as the principle photon-emitting nuclide. ²¹²Bi and ²⁰⁸Tl emit high-energy photons up to 2,615 keV. A phantom study was conducted to determine the optimal acquisition and reconstruction protocol. Methods: The spheres of a body phantom were filled with a ²²⁴Ra-RaCl₂ solution, and the background compartment was filled with water. Images were acquired on a SPECT/CT system. In addition, 30-min scans were acquired for 80- and 240-keV emissions, using triple-energy windows, with both medium-energy and high-energy collimators. Images were acquired at 90-95 and 29-30 kBq/mL, plus an explorative 3-min acquisition at 20 kBq/mL (using only the optimal protocol). Reconstructions were performed with attenuation correction only, attenuation plus scatter correction, 3 levels of postfiltering, and 24 levels of iterative updates. Acquisitions and reconstructions were compared using the maximum value and signal-to-scatter peak ratio for each sphere. Monte Carlo simulations were performed to examine the contributions of key emissions. Results: Secondary photons of the 2,615-keV ²⁰⁸Tl emission produced in the collimators make up most of the acquired energy spectrum, as revealed by Monte Carlo simulations, with only a small fraction (3%-6%) of photons in each window providing useful information for imaging. Still, decent image quality is possible at 30 kBq/mL, and nuclide concentrations are imageable down to approximately 2-5 kBq/mL. The overall best results were obtained with the 240-keV window, medium-energy collimator, attenuation and scatter correction, 30 iterations and 2 subsets, and a 12-mm gaussian postprocessing filter. However, all combinations of the applied collimators and energy windows were capable of producing adequate results, even though some failed to reconstruct the 2 smallest spheres. Conclusion: SPECT/CT imaging of ²²⁴Ra in equilibrium with daughters is possible, with sufficient image quality to provide clinical utility for the current trial of intraperitoneally administrated activity. A systematic scheme for optimization was designed to select acquisition and reconstruction settings.

Simulation of X-ray projections on GPU: Benchmarking gVirtualXray with clinically realistic phantoms

BACKGROUND AND OBJECTIVES

This study provides a quantitative comparison of images created using gVirtualXray (gVXR) to both Monte Carlo (MC) and real images of clinically realistic phantoms. gVirtualXray is an open-source framework that relies on the Beer-Lambert law to simulate X-ray images in realtime on a graphics processor unit (GPU) using triangular meshes.

METHODS

Images are generated with gVirtualXray and compared with a corresponding ground truth image of an anthropomorphic phantom: (i) an X-ray projection generated using a Monte Carlo simulation code, (ii) real digitally reconstructed radiographs (DRRs), (iii) computed tomography (CT) slices, and (iv) a real radiograph acquired with a clinical X-ray imaging system. When real images are involved, the simulations are used in an image registration framework so that the two images are aligned.

RESULTS

The mean absolute percentage error (MAPE) between the images simulated with gVirtualXray and MC is 3.12%, the zero-mean normalised cross-correlation (ZNCC) is 99.96% and the structural similarity index (SSIM) is 0.99. The run-time is 10 days for MC and 23 ms with gVirtualXray. Images simulated using surface models segmented from a CT scan of the Lungman chest phantom were similar to (i) DRRs computed from the CT volume and (ii) an actual digital radiograph. CT slices reconstructed from images simulated with gVirtualXray were comparable to the corresponding slices of the original CT volume.

CONCLUSIONS

When scattering can be ignored, accurate images that would take days using MC can be generated in milliseconds with gVirtualXray. This speed of execution enables the use of repetitive simulations with varying parameters, e.g. to generate training data for a deep-learning algorithm, and to minimise the objective function of an optimisation problem in image registration. The use of surface models enables the combination of X-ray simulation with real-time soft-tissue deformation and character animation, which can be deployed in virtual reality applications.

FETAL DOSE ESTIMATION DURING PREGNANCY USING GATE MONTE CARLO SIMULATION: APPLICATION OF HODGKIN'S LYMPHOMA RADIOTHERAPY

The aim of this study is to estimate the fetal radiation dose for a pregnant patient treated for Hodgkin's lymphoma. Due to the supradiaphragmatic extensions, two plans are used for this treatment, one for supra-clavicular and the other for cervical lymph nodes, with beam energies of 18 and 6 MV, respectively. We model the ELEKTA accelerator (Versa HD Ltd, Crawly, UK) and the pregnant patient using GATE code. The accelerator is modelled based on the vendor-supplied data and the pregnant patient is modeled with a voxelized pregnant woman phantom (Katja, 29 years old) at the 24th week of pregnancy. In each plan, we estimate the absorbed dose of each fetus organ by delivering a 2 Gy for one fraction and then multiplying the result by 15 fractions to get the total prescribed dose, then we calculate the mean fetal absorbed dose. The results indicate that the mean absorbed fetal dose was 26.18 mGy.

Mesh modeling of system geometry and anatomy phantoms for realistic GATE simulations and their inclusion in SPECT reconstruction

Objective.Monte-Carlo simulation studies have been essential for advancing various developments in single photon emission computed tomography (SPECT) imaging, such as system design and accurate image reconstruction. Among the simulation software available, Geant4 application for tomographic emission (GATE) is one of the most used simulation toolkits in nuclear medicine, which allows building systems and attenuation phantom geometries based on the combination of idealized volumes. However, these idealized volumes are inadequate for modeling free-form shape components of such geometries. Recent GATE versions alleviate these major limitations by allowing users to import triangulated surface meshes.Approach.In this study, we describe our mesh-based simulations of a next-generation multi-pinhole SPECT system dedicated to clinical brain imaging, called AdaptiSPECT-C. To simulate realistic imaging data, we incorporated in our simulation the XCAT phantom, which provides an advanced anatomical description of the human body. An additional challenge with the AdaptiSPECT-C geometry is that the default voxelized XCAT attenuation phantom was not usable in our simulation due to intersection of objects of dissimilar materials caused by overlap of the air containing regions of the XCAT beyond the surface of the phantom and the components of the imaging system.Main results.We validated our mesh-based modeling against the one constructed by idealized volumes for a simplified single vertex configuration of AdaptiSPECT-C through simulated projection data of123I-activity distributions. We resolved the overlap conflict by creating and incorporating a mesh-based attenuation phantom following a volume hierarchy. We then evaluated our reconstructions with attenuation and scatter correction for projections obtained from simulation consisting of mesh-based modeling of the system and the attenuation phantom for brain imaging. Our approach demonstrated similar performance as the reference scheme simulated in air for uniform and clinical-like123I-IMP brain perfusion source distributions.Significance.This work enables the simulation of complex SPECT acquisitions and reconstructions for emulating realistic imaging data close to those of actual patients.

Dosimetric evaluation of an intraoperative radiotherapy system: a measurement-based and Monte-Carlo modelling investigation

Intraoperative radiotherapy (IORT) is a specialised subset of radiotherapy, where a high radiation dose is delivered to a surgically exposed tumour bed in order to eradicate any remaining cancer cells. The aim of this study was to examine the dose characteristics of the Zeiss Intrabeam IORT device which provides near-isotropic emission of up to 50 kV X-rays. The EGSnrc Monte Carlo (MC) code system was used to simulate the device and percentage depth dose (PDD) data measured with a soft X-ray parallel-plate ionisation chamber were used for model verification. The model provided energy spectra, isodose curves and mean photon energies. In addition, EBT3 Gafchromic film was used to verify the MC model by examining PDDs and 2D dose distributions for various applicators. The differences between MC model and ionisation chamber measurements were within 3% for most points, with a maximum deviation of ~ 9%. Most of the simulated PDD points were within 5% of the film-measured data, with a maximum deviation of ~ 10%. The mean energy of the bare probe was found to be 21.19 keV. The mean photon energy from applicators ranged from 29.00 to 30.85 keV. Results of this study may be useful for future work on creating a system for treatment planning.

Publicly available framework for simulating and experimentally validating clinical PET systems

BACKGROUND

Monte Carlo (MC) simulations are a powerful tool to model medical imaging systems. However, before simulations can be considered the ground truth, they have to be validated with experiments.

PURPOSE

To provide a pipeline that models a clinical positron emission tomography (PET)/CT system using MC simulations after extensively validating the results against experimental measurements.

METHODS

A clinical four-ring PET imaging system was modeled using Geant4 application for tomographic emission (v. 9.0). To validate the simulations, PET images were acquired of a cylindrical phantom, point source, and image quality phantom with the modeled system and the simulations of the experimental procedures. For the purpose of validating the quantification capabilities and image quality provided by the simulation pipeline, the simulations were compared against the measurements in terms of their count rates and sensitivity as well as their image uniformity, resolution, recovery coefficients (RCs), coefficients of variation, contrast, and background variability.

RESULTS

When compared to the measured data, the number of true detections in the MC simulations was within 5%. The scatter fraction was found to be 30.0% ± 2.2% and 28.8% ± 1.7% in the measured and simulated scans, respectively. Analyzing the measured and simulated sinograms, the sensitivities were found to be 8.2 and 7.8 cps/kBq, respectively. The fraction of random coincidences were 19% in the measured data and 25% in the simulation. When calculating the image uniformity within the axial slices, the measured image exhibited a uniformity of 0.015 ± 0.005, whereas the simulated image had a uniformity of 0.029 ± 0.011. In the axial direction, the uniformity was measured to be 0.024 ± 0.006 and 0.040 ± 0.015 for the measured and simulated data, respectively. Comparing the image resolution, an average percentage difference of 2.9% was found between the measurements and simulations. The RCs calculated in both the measured and simulated images were found to be within the EARL ranges, except for that of the simulation of the smallest sphere. The coefficients of variation for the measured and simulated images were found to be 12% and 13%, respectively. Lastly, the background variability was consistent between the measurements and simulations, whereas the average percentage difference in the sphere contrasts was found to be 8.8%.

CONCLUSION

The clinical PET/CT system was modeled and validated to provide a simulation pipeline for the community. The pipeline and the validation procedures have been made available (https://github.com/teaghan/PET_MonteCarlo).

Dose Estimation for Extravasation of 177Lu, 99mTc, and 18F

ABSTRACT

Extravasation is the situation in which a nuclear medicine injection deposits some fraction of its radioactivity into the soft tissue rather than the bloodstream and may result in a large local radiation dose to tissue. An understanding of localized radiation dose from such unexpected events can be an important aspect of clinical radiation protection. The aim of this study was to estimate and assess absorbed radiation dose to localized soft tissue for hypothetical scenarios of radiopharmaceutical extravasation. Specifically, the goal was to understand whether a radiopharmaceutical extravasation could exceed the US Nuclear Regulatory Commission's medical event reporting limit of 0.5 Sv dose equivalent to tissue or levels at which tissue damage would be anticipated (1.0 Sv dose equivalent). We used the GATE Monte Carlo simulation software to calculate self-dose to spherical volumes containing uniformly distributed amounts of common radiopharmaceutical isotopes. Simulated volumes, radioactivity levels, and effective half-lives represented real-world nuclear medicine procedures. Chosen scenarios consisted of 50 mCi and 100 mCi 177Lu within 20 cm3 and 40 cm3 tissue volumes and a 60 min biological clearance half-time (59.6 min effective half-life), 6 mCi and 12 mCi 99mTc within 1 cm3 and 5 cm3 tissue volumes and a 120 min biological clearance half-time (90 min effective half-life), and 3 mCi and 6 mCi 18F within 1 cm3 and 5 cm3 tissue volumes with a 30 min biological clearance half-time (23.6 min effective half-life). We calculated absorbed doses to be between 5.5 Gy and 23.5 Gy for 177Lu, between 0.9 Gy and 12.4 Gy for 99mTc, and between 1.5 Gy and 16.2 Gy for 18F. Radiopharmaceutical extravasations can result in tissue doses that surpass both medical event reporting limits and levels at which deterministic effects are expected. Radiation safety programs should include identification, mitigation, dosimetry, and documentation of significant extravasation events.

Cobalt compensator-based IMRT device: A treatment planning study of head and neck cases

PURPOSE

Our goal is to develop a novel cobalt-compensator-based IMRT device for low- and middle-income countries that is reliable and cost-effective while delivering treatment plans of equal quality to those from linac-MLC devices. The present study examines the quality of treatment plans using this device.

METHODS

A commercial treatment planning system (TPS; RayStation v.8B) was commissioned for this device using Monte Carlo simulations from the Geant4 toolkit. Patient-specific compensators were created as regions-of-interest. Thirty clinical head & neck cases were planned and compared to clinical plans with a 6MV linac using IMRT. The mock head and neck plan from TG-119 was used for further validation.

RESULTS

PTV objectives were achieved in all 30 plans with PTV V95% >95 %. OAR sparing was similar to clinical plans. There were 14 cases where OAR dose limits exceeded the recommended QUANTEC limits in the clinical plan in order to achieve target coverage. OAR sparing was better in the cobalt compensator plan in 8 cases and worse in 3 cases, in the latter cases exceeding the clinical plan doses by an average of 8.22 % (0.0 %-13.5 %). Average field-by-field gamma pass-rate were 93.7 % (2 %/2mm). Estimated treatment times using the Co-60 compensator device were 1 min 27 s vs 1 min 2 s for the clinical system.

CONCLUSION

This system is the first of its kind to allow for IMRT with a Co-60 device. Data here suggests that the delivery meets plan quality criteria while maintaining short treatment times which may offer a sustainable and cost-low option for IMRT on the global scale.

Development of a voxel S-value database for patient internal radiation dosimetry

PURPOSE

Personalized dosimetry with high accuracy drew great attention in clinical practices. Voxel S-value (VSV) convolution has been proposed to speed up absorbed dose calculations. However, the VSV method is efficient for personalized internal radiation dosimetry only when there are pre-calculated VSVs of the radioisotope. In this work, we propose a new method for VSV calculation based on the developed mono-energetic particle VSV database of γ, β, α, and X-ray for any radioisotopes.

METHODS

Mono-energetic VSV database for γ, β, α, and X-ray was calculated using Monte Carlo methods. Radiation dose was first calculated based on mono-energetic VSVs for [F-18]-FDG in 10 patients. The estimated doses were compared with the values obtained from direct Monte Carlo simulation for validation of the proposed method. The number of VSVs used in calculation was optimized based on the estimated dose accuracy and computation time.

RESULTS

The generated VSVs showed a great consistency with the results calculated using direct Monte Carlo simulation. For [F-18]-FDG, the proposed VSV method with number of VSV of 9 shows the best relative average organ absorbed dose uncertainty of 3.25% while the calculation time was reduced by 99% and 97% compared to the Monte Carlo simulation and traditional multiple VSV methods, respectively.

CONCLUSIONS

In this work, we provided a method to generate the VSV kernels for any radioisotope based on the pre-calculated mono-energetic VSV database and significantly reduced the time cost for the multiple VSVs dosimetry approach. A software was developed to generate VSV kernels for any radioisotope in 19 mediums.

Commissioning a beam line for MR-guided particle therapy assisted by in silico methods

BACKGROUND

Radiation therapy is continuously moving towards more precise dose delivery. The combination of online MR imaging and particle therapy, for example, radiation therapy using protons or carbon ions, could enable the next level of precision in radiotherapy. In particle therapy, research towards a combination of MR and particle therapy is well underway, but still far from clinical systems. The combination of high magnetic fields with particle therapy delivery poses several challenges for treatment planning, treatment workflow, dose delivery, and dosimetry.

PURPOSE

To present a workflow for commissioning of a light ion beam line with an integrated dipole magnet to perform MR-guided particle therapy (MRgPT) research, producing not only basic beam data but also magnetic field maps for accurate dose calculation. Accurate dose calculation in magnetic field environments requires high-quality magnetic field maps to compensate for magnetic-field-dependent trajectory changes and dose perturbations.

METHODS

The research beam line at MedAustron was coupled with a resistive dipole magnet positioned at the isocenter. Beam data were measured for proton and carbon ions with and without an applied magnetic field of 1 T. Laterally integrated depth-dose curves (IDC) as well as beam profiles were measured in water while beam trajectories were measured in air. Based on manufacturer data, an in silico model of the magnet was created, allowing to extract high-quality 3D magnetic field data. An existing GATE/Geant4 Monte Carlo (MC) model of the beam line was extended with the generated magnetic field data and benchmarked against experimental data.

RESULTS

A 3D magnetic field volume covering fringe fields until 50 mT was found to be sufficient for an accurate beam trajectory modeling. The effect on particle range retraction was found to be 2.3 and 0.3 mm for protons and carbon ions, respectively. Measured lateral beam offsets in water agreed within 0.4 and -0.5 mm with MC simulations for protons and carbon ions, respectively. Experimentally determined in-air beam trajectories agreed within 0.4 mm in the homogeneous magnetic field area.

CONCLUSION

The presented approach based on in silico modeling and measurements allows to commission a beam line for MRgPT while providing benchmarking data for the magnetic field modeling, required for state-of-the art dose calculation methods.

GATE/Geant4-based dosimetry for ex vivo in solution irradiation of blood with radionuclides

To establish a dose-response relationship between radiation-induced DNA damage and the corresponding absorbed doses in blood irradiated with radionuclides in solution under ex vivo conditions, the absorbed dose coefficient for 1 ml for 1 h internal ex vivo irradiation of peripheral blood (d_Blood) must be determined. d_Blood is specific for each radionuclide, and it depends on the irradiation geometry. Therefore, the aim of this study is to use the Monte Carlo radiation transport code GATE/Geant4 to calculate the mean absorbed dose rates for ex vivo irradiation of blood with several radionuclides used in Nuclear Medicine.

METHODS

The Monte Carlo simulation reproduces the irradiation geometry of a blood sample of 7 ml mixed with 1 ml of a water equivalent radioactive solution in an 8 ml vial. The simulation was performed for ten different radionuclides: ¹⁸F, ⁶⁸Ga, ⁹⁰Y, ^99mTc, ¹²³I, ¹²⁴I, ¹³¹I, ¹⁷⁷Lu, ²²³Ra, and ²²⁵Ac. Two sets of simulations for each radionuclide were performed with 1x10⁹ histories. The first set was simulated with a mass density of 1.0525 g/cm³ of the blood plus water mixture. The second set of simulations was performed with a mass density of 1 g/cm³ for comparison with previous studies.

RESULTS

The values of d_Blood for ten radionuclides were calculated. The values range from 10.23 mGy∙ml∙MBq^-1 for ^99mTc to 15632.02 mGy∙ml∙MBq^-1 for ²²⁵Ac. The maximum relative change compared to previous studies was 13.0% for ¹²⁴I.

CONCLUSION

This study provides a comprehensive set of absorbed dose coefficients for 1 ml for 1 h internal ex vivo irradiation of peripheral blood in a special vial geometry and radionuclides typically used in Nuclear Medicine. Furthermore, the method proposed by this work can be easily adapted to a variety of internal irradiation conditions and serve as a reference for future studies.

Design and performance evaluation of a slit-slat camera for 2D prompt gamma imaging in proton therapy monitoring: A Monte Carlo simulation study

PURPOSE

We investigated the design of a prompt gamma camera for real-time dose delivery verification and the partial mitigation of range uncertainties.

METHODS

A slit slat (SS) camera was optimized using the trade-off between the signal-to-noise ratio and spatial resolution. Then, using the GATE Monte Carlo package, the camera performances were estimated by means of target shifts, beam position quantification, changing the camera distance from the beam, and air cavity inserting. A homogeneous PMMA phantom and the air gaps induced PMMA phantom were used. The air gaps ranged from 5 mm to 30 mm by 5 mm increments were positioned in the middle of the beam range. To reduce the simulation time, phase space scoring was used. The batch method with five realizations was used for stochastic error calculations.

RESULTS

The system's detection efficiency was 1.1 × 10 - 4 PGs Emitted PGs ( 1.8 × 10 - 5 $1.1 \times {10}^{-4}\frac{{\rm PGs}}{{\rm Emitted}\ {\rm PGs}}\ (1.8 \times {10}^{-5}$ PGs/proton) for a 10 × 20 cm² detector (source-to-collimator distance = 15.0 cm). Axial and transaxial resolutions were 23 mm and 18 mm, respectively. The SS camera estimated the range as 69.0 ± 3.4 (relative stochastic error 1-sigma is 5%) and 67.6 ± 1.8 mm (2.6%) for the real range of 67.0 mm for 10⁷ and 10⁸ protons of 100 MeV, respectively. Considering 160 MeV, these values are 155.5 ± 3.1 (2%) and 152.2 ± 2.0 mm (1.3%) for the real range of 152.0 mm for 10⁷ and 10⁸ protons, respectively. Considering phantom shift, for a 100 MeV beam, the precision of the quantification (1-sigma) in the axial and lateral phantom shift estimation is 2.6 mm and 1 mm, respectively. Accordingly, the axial and lateral quantification precisions were 1.3 mm and 1 mm for a 160 MeV beam, respectively. Furthermore, the quantification of an air gap formulated as gap d e t = 0.98 × gap real ${{\rm gap}}_{det}=0.98 \times {{\rm gap}}_{{\rm real}}$ , where gap d e t ${{\rm gap}}_{det}$ and gap_real are the estimated and real air gap, respectively. The precision of the air gap quantification is 1.6 mm (1 sigma). Moreover, 2D PG images show the trajectory of the proton beam through the phantom.

CONCLUSION

The proposed slit-slat imaging systems can potentially provide a real-time, in-vivo, and non-invasive treatment monitoring method for proton therapy.

Monte-Carlo techniques for radiotherapy applications I: introduction and overview of the different Monte-Carlo codes

Introduction:

The dose calculation plays a crucial role in many aspects of contemporary clinical radiotherapy treatment planning process. It therefore goes without saying that the accuracy of the dose calculation is of very high importance. The gold standard for absorbed dose calculation is the Monte-Carlo algorithm.

Methods:

This first of two papers gives an overview of the main openly available and supported codes that have been widely used for radiotherapy simulations.

Results:

The paper aims to provide an overview of Monte-Carlo in the field of radiotherapy and point the reader in the right direction of work that could help them get started or develop their existing understanding and use of Monte-Carlo algorithms in their practice.

Conclusions:

It also serves as a useful companion to a curated collection of papers on Monte-Carlo that have been published in this journal.

First experimental measurements of 2D microdosimetry maps in proton therapy

BACKGROUND

Empirical data in proton therapy indicate that relative biological effectiveness (RBE) is not constant, and it is directly related to the linear energy transfer (LET). The experimental assessment of LET with high resolution would be a powerful tool for minimizing the LET hot spots in intensity-modulated proton therapy, RBE- or LET-guided evaluation and optimization to achieve biologically optimized proton plans, verifying the theoretical predictions of variable proton RBE models, and so on. This could impact clinical outcomes by reducing toxicities in organs at risk.

PURPOSE

The present work shows the first 2D LET maps obtained at a proton therapy facility using the double scattering delivery mode in clinical conditions by means of new silicon 3D-cylindrical microdetectors.

METHODS

The device consists of a matrix of 121 independent silicon-based detectors that have 3D-cylindrical electrodes of 25-µm diameter and 20-µm depth, resulting each one of them in a well-defined micrometric radiation sensitive volume etched inside the silicon. They have been specifically designed for a hadron therapy, improving the performance of current silicon-based microdosimeters. Microdosimetry spectra were obtained at different positions of the Bragg curve by using a water-equivalent phantom along an 89-MeV pristine proton beam generated in the Y1 proton passive scattering beamline of the Orsay Proton Therapy Centre (Institut Curie, France).

RESULTS

Microdosimetry 2D-maps showing the variation of the lineal energy with depth in the three dimensions were obtained in situ during irradiation at clinical fluence rates (∼10⁸  s^-1  cm^-2 ) for the first time with a spatial resolution of 200 µm, the highest achieved in the transverse plane so far. The experimental results were cross-checked with Monte Carlo simulations and a good agreement between the spectra shapes was found. The experimental frequency-mean lineal energy values in silicon were 1.858 ± 0.019 keV µm^-1 at the entrance, 2.61 ± 0.03 keV µm^-1 at the proximal distance, 4.97 ± 0.05 keV µm^-1 close to the Bragg peak, and 8.6 ± 0.1 keV µm^-1 at the distal edge. They are in good agreement with the expected trends in the literature in clinical proton beams.

CONCLUSIONS

We present the first 2D microdosimetry maps obtained in situ during irradiation at clinical fluence rates in proton therapy. Our results show that the arrays of 3D-cylindrical microdetectors are a reliable microdosimeter to evaluate LET maps not only in the longitudinal axis of the beam, but also in the transverse plane allowing for LET characterization in three dimensions. This work is a proof of principle showing the capacity of our system to deliver LET 2D maps. This kind of experimental data is needed to validate variable proton RBE models and to optimize LET-guided plans.

Monte-Carlo techniques for radiotherapy applications II: equipment and source modelling, dose calculations and radiobiology

Introduction:

This is the second of two papers giving an overview of the use of Monte-Carlo techniques for radiotherapy applications.

Methods:

The first paper gave an introduction and introduced some of the codes that are available to the user wishing to model the different aspects of radiotherapy treatment. It also aims to serve as a useful companion to a curated collection of papers on Monte-Carlo that have been published in this journal.

Results and Conclusions:

This paper focuses on the application of Monte-Carlo to specific problems in radiotherapy. These include radiotherapy and imaging beam production, brachytherapy, phantom and patient dosimetry, detector modelling and track structure calculations for micro-dosimetry, nano-dosimetry and radiobiology.

Accuracy of Acuros[Formula: see text] BV as determined from GATE monte-carlo simulation

The American Association of Physicists in Medicine's Task Group No.43 has provided a standardised dose calculation methodology that is now the international benchmark for all brachytherapy dosimetry publications and treatment planning systems. However, limitations in this methodology has seen the development of Model-Based Dose Calculation Algorithms (MBDCA). In 2009, Varian (Varian Medical Systems, Palo Alto, CA, USA) released Acuros[Formula: see text] BrachyVision (ABV) which calculates dose by explicitly solving the Linear Boltzmann Transport Equation. In this study we have assessed the accuracy of ABV dose calculations within a range of materials relevant to high dose rate brachytherapy with an iridium-192 ([Formula: see text]Ir) source. Accuracy assessment has been achieved by implementing a modelled GamaMed Plus [Formula: see text]Ir source within a series of phantoms using the GEANT4 Application for Emission Tomography (GATE) to calculate dose for comparison with dose as determined by ABV. Comparisons between GATE and ABV were made using point-to-point profile comparisons and 1D gamma analysis. Source validation results yielded good agreement with published data. Spectrum and TG43U1 comparisons showed no major differences, with TG43U1 comparisons agreeing within ± 1%. Point-to-point comparisons showed large differences between GATE and ABV near the source and in low density materials. 1D gamma analysis pass criteria of 2%/1 mm and 2%/2 mm yielded passing rates ranging between 51.72-100% and 62.07-100% respectively. A critical analysis of this study's results suggest that ABV is unable to accurately calculate doses in low density materials. Furthermore, spatial accuracy of dose near the source is within 2 mm.

Deep learning-based segmentation in prostate radiation therapy using Monte Carlo simulated cone-beam computed tomography

PURPOSE

Segmenting organs in cone-beam CT (CBCT) images would allow to adapt the radiotherapy based on the organ deformations that may occur between treatment fractions. However, this is a difficult task because of the relative lack of contrast in CBCT images, leading to high inter-observer variability. Deformable image registration (DIR) and deep-learning based automatic segmentation approaches have shown interesting results for this task in the past years. However, they are either sensitive to large organ deformations, or require to train a convolutional neural network (CNN) from a database of delineated CBCT images, which is difficult to do without improvement of image quality. In this work, we propose an alternative approach: to train a CNN (using a deep learning-based segmentation tool called nnU-Net) from a database of artificial CBCT images simulated from planning CT, for which it is easier to obtain the organ contours.

METHODS

Pseudo-CBCT (pCBCT) images were simulated from readily available segmented planning CT images, using the GATE Monte Carlo simulation. CT reference delineations were copied onto the pCBCT, resulting in a database of segmented images used to train the neural network. The studied segmentation contours were: bladder, rectum, and prostate contours. We trained multiple nnU-Net models using different training: (1) segmented real CBCT, (2) pCBCT, (3) segmented real CT and tested on pseudo-CT (pCT) generated from CBCT with cycleGAN, and (4) a combination of (2) and (3). The evaluation was performed on different datasets of segmented CBCT or pCT by comparing predicted segmentations with reference ones thanks to Dice similarity score and Hausdorff distance. A qualitative evaluation was also performed to compare DIR-based and nnU-Net-based segmentations.

RESULTS

Training with pCBCT was found to lead to comparable results to using real CBCT images. When evaluated on CBCT obtained from the same hospital as the CT images used in the simulation of the pCBCT, the model trained with pCBCT scored mean DSCs of 0.92 ± 0.05, 0.87 ± 0.02, and 0.85 ± 0.04 and mean Hausdorff distance 4.67 ± 3.01, 3.91 ± 0.98, and 5.00 ± 1.32 for the bladder, rectum, and prostate contours respectively, while the model trained with real CBCT scored mean DSCs of 0.91 ± 0.06, 0.83 ± 0.07, and 0.81 ± 0.05 and mean Hausdorff distance 5.62 ± 3.24, 6.43 ± 5.11, and 6.19 ± 1.14 for the bladder, rectum, and prostate contours, respectively. It was also found to outperform models using pCT or a combination of both, except for the prostate contour when tested on a dataset from a different hospital. Moreover, the resulting segmentations demonstrated a clinical acceptability, where 78% of bladder segmentations, 98% of rectum segmentations, and 93% of prostate segmentations required minor or no corrections, and for 76% of the patients, all structures of the patient required minor or no corrections.

CONCLUSION

We proposed to use simulated CBCT images to train a nnU-Net segmentation model, avoiding the need to gather complex and time-consuming reference delineations on CBCT images.

Impact of radiopharmaceutical therapy (177Lu, 225Ac) microdistribution in a cancer-associated fibroblasts model

Background

The aim of this study is to elucidate the difference in absorbed dose (D_abs) patterns in radiopharmaceutical therapies between alpha emitters (²²⁵Ac) and beta emitters (¹⁷⁷Lu) when targeting cancer-associated fibroblasts (CAF) or tumor cells. Five spherical models with 3 mm diameter were created, representing spherical tumor masses that contain tumor clusters, interspersed with CAFs. The mean distance from a tumor cell to the nearest CAF (L_mean) varied throughout these models from 92 to 1030 µm. D_abs calculations were performed while selecting either CAFs or tumor cells as sources, with Convolution/Superposition with ¹⁷⁷Lu and Monte Carlo simulations (GATE) with ²²⁵Ac. Analyses were conducted with Dose Volume Histograms and efficacy ratios (ER), which represents the ratio of mean D_abs that is deposited in the target volume.

Results

²²⁵Ac is the most optimal radionuclide when CAFs are both targeted and irradiating themselves, as ERs increase from 1.5 to 3.7 when L_mean increases from 92 to 1030 µm. With ¹⁷⁷Lu, these numbers vary from 1.2 to 2.7. Conversely, when CAFs are sources and tumors are targets with ²²⁵Ac, ERs decreased from 0.8 to 0.1 when L_mean increases from 92 to 1030 µm. With ¹⁷⁷Lu, these numbers vary from 0.9 to 0.3

Conclusion

When targeting CAFs to irradiate tumors, the efficacy of using ²²⁵Ac decreases as the average size of the tumor clusters (or L_mean) increases. In such situations, ¹⁷⁷Lu will be more effective than ²²⁵Ac when targeting CAFs due to the longer beta particle range.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40658-022-00497-5.

The OpenGATE ecosystem for Monte Carlo simulation in medical physics

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.