A photon source model for alpha-emitter radionuclides.

Objective.A Monte Carlo virtual source model named PHID (photon from Ion decay) that generates photons emitted in the complex decay chain process of alpha-emitter radionuclides is proposed, typically for use during the simulation of SPECT image acquisition.Approach.Given an alpha-emitter radionuclide, the PHID model extracts from Geant4 databases the photon emission lines from all decaying daughters for both isometric transition and atomic relaxation processes. According to a given time range, abundances and activities in the decay chain are considered thanks to the Bateman equations, taking into account the decay rates and the initial abundances.Main results.PHID is evaluated by comparison with analog Monte Carlo simulation. It generates photons with the correct energy and temporal distribution, avoiding the costly simulation of the complete decay chain thus decreasing the computation time. The exact time gain depends on the simulation setup. As an example, it is 30× faster for simulating 1 MBq of225Ac in water for 1 section Moreover, for225Ac, PHID was also compared to a simplified source model with the two main photon emission lines (218 and 440 keV). PHID shows that 2 times more particles are simulated and 60% more counts are detected in the images.Significance.PHID can simulate any alpha-emitter radionuclide available in the Geant4 database. As a limitation, photons emitted from Bremsstrahlung are ignored, but they represent only 0.7% of the photons above 30 keV and are not significant for SPECT imaging. PHID is open-source, available in GATE 10, and eases the investigation of imaging photon emission from alpha emitters.

Annihilation photon GAN source model for PET Monte Carlo simulation.

Objective.Following previous works on virtual sources model with Generative Adversarial Network (GAN), we extend the proof of concept for generating back-to-back pairs of gammas with timing information, typically for Monte Carlo simulation of Positron Emission Tomography(PET) imaging.Approach.A conditional GAN is trained once from a low statistic simulation in a given attenuation phantom and enables the generation of various activity source distributions. GAN training input is a set of gammas exiting a phantom, tracked from a source of positron emitters, described by position, direction and energy. A new parameterization that improves the training is also proposed. An ideal PET reconstruction algorithm is used to evaluate the quality of the GAN.Main results.The proposed method is evaluated on National Electrical Manufacturers Association (NEMA) International Electrotechnical Commission (IEC) phantoms and with CT patient image showing good agreement with reference simulations. The proportions of 2-gammas, 1-gammas and absorbed-gammas are respected to within one percent, image profiles matched and recovery coefficients were close with less than 5% difference. GAN tends to blur gamma energy peak, e.g. 511 keV.Significance.Once trained, the GAN generator can be used as input source for Monte Carlo simulations of PET imaging systems, decreasing the computational time with speedups up to ×400 according to the configurations.

Analytical modeling and Monte Carlo simulations of multi-parallel slit and knife-edge slit prompt gamma cameras.

Purpose.Present and validate an analytical model (AM) to calculate efficiency and spatial resolution of multi-parallel slit (MPS) and knife-edge slit (KES) cameras in the context of prompt gamma (PG) imaging in proton therapy, as well as perform a fair comparison between two prototypes of these cameras with their design specifications.Materials and methods.Monte Carlo (MC) simulations with perfect (ideal) conditions were performed to validate the proposed AM, as well as simulations in realistic conditions for the comparison of both prototypes. The spatial resolution obtained from simulations was derived from reconstructed PG profiles. The falloff retrieval precision (FRP) was quantified based on the variability of PG profiles from 50 different realizations.Results.The AM shows that KES and MPS designs fulfilling 'MPS-KES similar conditions' should have very close actual performances if the KES slit width corresponds to the half of the MPS slit width. Reconstructed PG profiles from simulated data with both cameras were used to compute the efficiency and spatial resolutions to compare against the model predictions. The FRP of both cameras was calculated with realistic detection conditions for beams with 107, 108and 109incident protons. A good agreement was found between the values predicted by the AM and those obtained from MC simulations (relative deviations of the order of 5%).Conclusion.The MPS camera outperforms the KES camera with their design specifications in realistic conditions and both systems can reach millimetric precision in the determination of the falloff position with 108or more initial protons.

Modeling complex particles phase space with GAN for Monte Carlo SPECT simulations: a proof of concept.

A method is proposed to model by a generative adversarial network the distribution of particles exiting a patient during Monte Carlo simulation of emission tomography imaging devices. The resulting compact neural network is then able to generate particles exiting the patient, going towards the detectors, avoiding costly particle tracking within the patient. As a proof of concept, the method is evaluated for single photon emission computed tomography (SPECT) imaging and combined with another neural network modeling the detector response function (ARF-nn). A complete rotating SPECT acquisition can be simulated with reduced computation time compared to conventional Monte Carlo simulation. It also allows the user to perform simulations with several imaging systems or parameters, which is useful for imaging system design.

Technical Note: GATE-RTion: a GATE/Geant4 release for clinical applications in scanned ion beam therapy.

PURPOSE: GATE-RTion is a validated version of GATE for clinical use in the field of light ion beam therapy. This paper describes the GATE-RTion project and illustrates its potential through clinical applications developed in three European centers delivering scanned proton and carbon ion treatments. METHODS: GATE-RTion is a collaborative framework provided by the OpenGATE collaboration. It contains a validated GATE release based on a specific Geant4 version, a set of tools to integrate GATE into a clinical environment and a network for clinical users. RESULTS: Three applications are presented: Proton radiography at the Centre Antoine Lacassagne (Nice, France); Independent dose calculation for proton therapy at the Christie NHS Foundation Trust (Manchester, UK); Independent dose calculation for protons and carbon ions at the MedAustron Ion Therapy center (Wiener Neustadt, Austria). CONCLUSIONS: GATE-RTion builds the bridge between researchers and clinical users from the OpenGATE collaboration in the field of Light Ion Beam Therapy. The applications presented in three European facilities using three completely different machines (three different vendors, cyclotron- and synchrotron-based systems, protons, and carbon ions) demonstrate the relevance and versatility of this project.

Mid-position treatment strategy for locally advanced lung cancer: a dosimetric study.

OBJECTIVE: The internal target volume (ITV) strategy generates larger planning target volumes (PTVs) in locally advanced non-small cell lung cancer (LA-NSCLC) than the Mid-position (Mid-p) strategy. We investigated the benefit of the Mid-p strategy regarding PTV reduction and dose to the organs at risk (OARs). METHODS: 44 patients with LA-NSCLC were included in a randomized clinical study to compare ITV and Mid-p strategies. GTV were delineated by a physician on maximum intensity projection images and on Mid-p images from four-dimensional CTs. CTVs were obtained by adding 6 mm uniform margin for microscopic extension. CTV to PTV margins were calculated using the van Herk's recipe for setup and delineation errors. For the Mid-p strategy, the mean target motion amplitude was added as a random error. For both strategies, three-dimensional conformal plans delivering 60-66 Gy to PTV were performed. PTVs, dose-volume parameters for OARs (lung, esophagus, heart, spinal cord) were reported and compared. RESULTS: With the Mid-p strategy, the median of volume reduction was 23.5 cm3 (p = 0.012) and 8.8 cm3 (p = 0.0083) for PTVT and PTVN respectively; the median mean lung dose reduction was 0.51 Gy (p = 0.0057). For 37.1% of the patients, delineation errors led to smaller PTV with the ITV strategy than with the Mid-p strategy. CONCLUSION: PTV and mean lung dose were significantly reduced using the Mid-p strategy. Delineation uncertainty can unfavorably impact the advantage. ADVANCES IN KNOWLEDGE: To the best of our knowledge, this is the first dosimetric comparison study between ITV and Mid-p strategies for LA-NSCLC.

CCMod: a GATE module for Compton camera imaging simulation.

Compton cameras are gamma-ray imaging systems which have been proposed for a wide variety of applications such as medical imaging, nuclear decommissioning or homeland security. In the design and optimization of such a system Monte Carlo simulations play an essential role. In this work, we propose a generic module to perform Monte Carlo simulations and analyses of Compton Camera imaging which is included in the open-source GATE/Geant4 platform. Several digitization stages have been implemented within the module to mimic the performance of the most commonly employed detectors (e.g. monolithic blocks, pixelated scintillator crystals, strip detectors...). Time coincidence sorter and sequence coincidence reconstruction are also available in order to aim at providing modules to facilitate the comparison and reproduction of the data taken with different prototypes. All processing steps may be performed during the simulation (on-the-fly mode) or as a post-process of the output files (offline mode). The predictions of the module have been compared with experimental data in terms of energy spectra, angular resolution, efficiency and back-projection image reconstruction. Consistent results within a 3-sigma interval were obtained for the energy spectra except for low energies where small differences arise. The angular resolution measure for incident photons of 1275 keV was also in good agreement between both data sets with a value close to 13°. Moreover, with the aim of demonstrating the versatility of such a tool the performance of two different Compton camera designs was evaluated and compared.

Generative adversarial networks (GAN) for compact beam source modelling in Monte Carlo simulations.

A method is proposed and evaluated to model large and inconvenient phase space files used in Monte Carlo simulations by a compact generative adversarial network (GAN). The GAN is trained based on a phase space dataset to create a neural network, called Generator (G), allowing G to mimic the multidimensional data distribution of the phase space. At the end of the training process, G is stored with about 0.5 million weights, around 10 MB, instead of a few GB of the initial file. Particles are then generated with G to replace the phase space dataset. This concept is applied to beam models from linear accelerators (linacs) and from brachytherapy seed models. Simulations using particles from the reference phase space on one hand and those generated by the GAN on the other hand were compared. 3D distributions of deposited energy obtained from source distributions generated by the GAN were close to the reference ones, with less than 1% of voxel-by-voxel relative difference. Sharp parts such as the brachytherapy emission lines in the energy spectra were not perfectly modeled by the GAN. Detailed statistical properties and limitations of the GAN-generated particles still require further investigation, but the proposed exploratory approach is already promising and paves the way for a wide range of applications.

Concepts and terms for dose/volume parameters in carbon-ion radiotherapy: Conclusions of the ULICE taskforce.

PURPOSE: The Union of Light Ion Centers in Europe (ULICE) program addressed the need for uniting scientific results for carbon-ion radiation therapy obtained by several institutions worldwide in different fields of excellence, and translating them into a real benefit to the community. Particularly, the concepts for dose/volume parameters developed in photon radiotherapy cannot be extrapolated to high linear energy transfer particles. METHODS AND MATERIALS: The ULICE-WP2 taskforce included radiation oncologists involved in carbon-ion radiation therapy and International Commission on Radiation Units and Measurements, radiation biologists, expert physicists in the fields of carbon-ion radiation therapy, microdosimetry, biological modeling and image-guided radiotherapy. Consensual reports emerged from multiple discussions within both the restricted group and the wider ULICE community. Public deliverables were produced and disseminated to the European Commission. RESULTS: Here we highlight the disparity in practices between treating centers, then address the main topics to finally elaborate specific recommendations. Although it appears relatively simple to add geometrical margins around the clinical target volume to obtain the planning target volume as performed in photon radiotherapy, this procedure is not appropriate for carbon-ion radiation therapy. Due to the variation of the radiation quality in depth, there is no generic relative biological effectiveness value for carbon-ions outside of an isolated point, for a given fractionation and specific experimental conditions. Absorbed dose and "equieffective dose" for specified conditions must always be reported. CONCLUSIONS: This work contributed to the development of standard operating procedures for carbon-ion radiation therapy clinical trials. These procedures are now being applied, particularly in the first phase III international, multicenter trial (PHRC Étoile).

Learning SPECT detector angular response function with neural network for accelerating Monte-Carlo simulations.

A method to speed up [Formula: see text] simulations of single photon emission computed tomography (SPECT) imaging is proposed. It uses an artificial neural network (ANN) to learn the angular response function (ARF) of a collimator-detector system. The ANN is trained once from a complete simulation including the complete detector head with collimator, crystal, and digitization process. In the simulation, particle tracking inside the SPECT head is replaced by a plane. Photons are stopped at the plane and the energy and direction are used as input to the ANN, which provides detection probabilities in each energy window. Compared to histogram-based ARF, the proposed method is less dependent on the statistics of the training data, provides similar simulation efficiency, and requires less training data. The implementation is available within the GATE platform.

Fixed forced detection for fast SPECT Monte-Carlo simulation.

Monte-Carlo simulations of SPECT images are notoriously slow to converge due to the large ratio between the number of photons emitted and detected in the collimator. This work proposes a method to accelerate the simulations based on fixed forced detection (FFD) combined with an analytical response of the detector. FFD is based on a Monte-Carlo simulation but forces the detection of a photon in each detector pixel weighted by the probability of emission (or scattering) and transmission to this pixel. The method was evaluated with numerical phantoms and on patient images. We obtained differences with analog Monte Carlo lower than the statistical uncertainty. The overall computing time gain can reach up to five orders of magnitude. Source code and examples are available in the Gate V8.0 release.

Optimization of GATE simulations for whole-body planar scintigraphic acquisitions using the XCAT male phantom with 177Lu-DOTATATE biokinetics in a Siemens Symbia T2.

Simulations of planar whole body acquisitions in therapeutic procedures are often extensively time-consuming and therefore rarely used. However, optimising tools and variance reduction techniques can be employed to overcome this problem. In this paper, a variety of features available in GATE are explored and their capabilities to reduce simulation time are evaluated. For this purpose, the male XCAT phantom was used as a virtual patient with 177Lu-DOTATATE pharmacokinetic for whole body planar acquisition simulations in a Siemens Symbia T2 model. Activity distribution was divided into 8 compartments that were simulated separately. GATE optimization techniques included reducing the amount of time spent in both voxel and detector tracking. Some acceleration techniques led to a decrease of CPU-time by a factor of 167, while image statistics were kept constant. In that context, the simulation of therapeutic procedure imaging would still require 46days on a single CPU, but this could be reduced to hours on a dedicated cluster.

Accelerated prompt gamma estimation for clinical proton therapy simulations.

There is interest in the particle therapy community in using prompt gammas (PGs), a natural byproduct of particle treatment, for range verification and eventually dose control. However, PG production is a rare process and therefore estimation of PGs exiting a patient during a proton treatment plan executed by a Monte Carlo (MC) simulation converges slowly. Recently, different approaches to accelerating the estimation of PG yield have been presented. Sterpin et al (2015 Phys. Med. Biol. 60 4915-46) described a fast analytic method, which is still sensitive to heterogeneities. El Kanawati et al (2015 Phys. Med. Biol. 60 8067-86) described a variance reduction method (pgTLE) that accelerates the PG estimation by precomputing PG production probabilities as a function of energy and target materials, but has as a drawback that the proposed method is limited to analytical phantoms. We present a two-stage variance reduction method, named voxelized pgTLE (vpgTLE), that extends pgTLE to voxelized volumes. As a preliminary step, PG production probabilities are precomputed once and stored in a database. In stage 1, we simulate the interactions between the treatment plan and the patient CT with low statistic MC to obtain the spatial and spectral distribution of the PGs. As primary particles are propagated throughout the patient CT, the PG yields are computed in each voxel from the initial database, as a function of the current energy of the primary, the material in the voxel and the step length. The result is a voxelized image of PG yield, normalized to a single primary. The second stage uses this intermediate PG image as a source to generate and propagate the number of PGs throughout the rest of the scene geometry, e.g. into a detection device, corresponding to the number of primaries desired. We achieved a gain of around 103 for both a geometrical heterogeneous phantom and a complete patient CT treatment plan with respect to analog MC, at a convergence level of 2% relative uncertainty in the 90% yield region. The method agrees with reference analog MC simulations to within 10-4, with negligible bias. Gains per voxel range from 102 to 104. The presented generic PG yield estimator is drop-in usable with any geometry and beam configuration. We showed a gain of three orders of magnitude compared to analog MC. With a large number of voxels and materials, memory consumption may be a concern and we discuss the consequences and possible tradeoffs. The method is available as part of Gate 7.2.

Validation of fast Monte Carlo dose calculation in small animal radiotherapy with EBT3 radiochromic films.

In preclinical studies, the absorbed dose calculation accuracy in small animals is fundamental to reliably investigate and understand observed biological effects. This work investigated the use of the split exponential track length estimator (seTLE), a new kerma based Monte Carlo dose calculation method for preclinical radiotherapy using a small animal precision micro irradiator, the X-RAD 225Cx. Monte Carlo modelling of the irradiator with GATE/GEANT4 was extensively evaluated by comparing measurements and simulations for half-value layer, percent depth dose, off-axis profiles and output factors in water and water-equivalent material for seven circular fields, from 20 mm down to 1 mm in diameter. Simulated and measured dose distributions in cylinders of water obtained for a 360° arc were also compared using dose, distance-to-agreement and gamma-index maps. Simulations and measurements agreed within 3% for all static beam configurations, with uncertainties estimated to 1% for the simulation and 3% for the measurements. Distance-to-agreement accuracy was better to 0.14 mm. For the arc irradiations, gamma-index maps of 2D dose distributions showed that the success rate was higher than 98%, except for the 0.1 cm collimator (92%). Using the seTLE method, MC simulations compute 3D dose distributions within minutes for realistic beam configurations with a clinically acceptable accuracy for beam diameter as small as 1 mm.

An empirical model for calculation of the collimator contamination dose in therapeutic proton beams.

Collimators are used as lateral beam shaping devices in proton therapy with passive scattering beam lines. The dose contamination due to collimator scattering can be as high as 10% of the maximum dose and influences calculation of the output factor or monitor units (MU). To date, commercial treatment planning systems generally use a zero-thickness collimator approximation ignoring edge scattering in the aperture collimator and few analytical models have been proposed to take scattering effects into account, mainly limited to the inner collimator face component. The aim of this study was to characterize and model aperture contamination by means of a fast and accurate analytical model. The entrance face collimator scatter distribution was modeled as a 3D secondary dose source. Predicted dose contaminations were compared to measurements and Monte Carlo simulations. Measurements were performed on two different proton beam lines (a fixed horizontal beam line and a gantry beam line) with divergent apertures and for several field sizes and energies. Discrepancies between analytical algorithm dose prediction and measurements were decreased from 10% to 2% using the proposed model. Gamma-index (2%/1 mm) was respected for more than 90% of pixels. The proposed analytical algorithm increases the accuracy of analytical dose calculations with reasonable computation times.

Monte Carlo simulation of prompt γ-ray emission in proton therapy using a specific track length estimator.

A Monte Carlo (MC) variance reduction technique is developed for prompt-γ emitters calculations in proton therapy. Prompt-γ emitted through nuclear fragmentation reactions and exiting the patient during proton therapy could play an important role to help monitoring the treatment. However, the estimation of the number and the energy of emitted prompt-γ per primary proton with MC simulations is a slow process. In order to estimate the local distribution of prompt-γ emission in a volume of interest for a given proton beam of the treatment plan, a MC variance reduction technique based on a specific track length estimator (TLE) has been developed. First an elemental database of prompt-γ emission spectra is established in the clinical energy range of incident protons for all elements in the composition of human tissues. This database of the prompt-γ spectra is built offline with high statistics. Regarding the implementation of the prompt-γ TLE MC tally, each proton deposits along its track the expectation of the prompt-γ spectra from the database according to the proton kinetic energy and the local material composition. A detailed statistical study shows that the relative efficiency mainly depends on the geometrical distribution of the track length. Benchmarking of the proposed prompt-γ TLE MC technique with respect to an analogous MC technique is carried out. A large relative efficiency gain is reported, ca. 10(5).

A track length estimator method for dose calculations in low-energy X-ray irradiations: implementation, properties and performance.

The track length estimator (TLE) method, an "on-the-fly" fluence tally in Monte Carlo (MC) simulations, recently implemented in GATE 6.2, is known as a powerful tool to accelerate dose calculations in the domain of low-energy X-ray irradiations using the kerma approximation. Overall efficiency gains of the TLE with respect to analogous MC were reported in the literature for regions of interest in various applications (photon beam radiation therapy, X-ray imaging). The behaviour of the TLE method in terms of statistical properties, dose deposition patterns, and computational efficiency compared to analogous MC simulations was investigated. The statistical properties of the dose deposition were first assessed. Derivations of the variance reduction factor of TLE versus analogous MC were carried out, starting from the expression of the dose estimate variance in the TLE and analogous MC schemes. Two test cases were chosen to benchmark the TLE performance in comparison with analogous MC: (i) a small animal irradiation under stereotactic synchrotron radiation therapy conditions and (ii) the irradiation of a human pelvis during a cone beam computed tomography acquisition. Dose distribution patterns and efficiency gain maps were analysed. The efficiency gain exhibits strong variations within a given irradiation case, depending on the geometrical (voxel size, ballistics) and physical (material and beam properties) parameters on the voxel scale. Typical values lie between 10 and 10(3), with lower levels in dense regions (bone) outside the irradiated channels (scattered dose only), and higher levels in soft tissues directly exposed to the beams.

Split exponential track length estimator for Monte-Carlo simulations of small-animal radiation therapy.

We propose the split exponential track length estimator (seTLE), a new kerma-based method combining the exponential variant of the TLE and a splitting strategy to speed up Monte Carlo (MC) dose computation for low energy photon beams. The splitting strategy is applied to both the primary and the secondary emitted photons, triggered by either the MC events generator for primaries or the photon interactions generator for secondaries. Split photons are replaced by virtual particles for fast dose calculation using the exponential TLE. Virtual particles are propagated by ray-tracing in voxelized volumes and by conventional MC navigation elsewhere. Hence, the contribution of volumes such as collimators, treatment couch and holding devices can be taken into account in the dose calculation.We evaluated and analysed the seTLE method for two realistic small animal radiotherapy treatment plans. The effect of the kerma approximation, i.e. the complete deactivation of electron transport, was investigated. The efficiency of seTLE against splitting multiplicities was also studied. A benchmark with analog MC and TLE was carried out in terms of dose convergence and efficiency.The results showed that the deactivation of electrons impacts the dose at the water/bone interface in high dose regions. The maximum and mean dose differences normalized to the dose at the isocenter were, respectively of 14% and 2% . Optimal splitting multiplicities were found to be around 300. In all situations, discrepancies in integral dose were below 0.5% and 99.8% of the voxels fulfilled a 1%/0.3 mm gamma index criterion. Efficiency gains of seTLE varied from 3.2 × 10(5) to 7.7 × 10(5) compared to analog MC and from 13 to 15 compared to conventional TLE.In conclusion, seTLE provides results similar to the TLE while increasing the efficiency by a factor between 13 and 15, which makes it particularly well-suited to typical small animal radiation therapy applications.

Machine learning-based patient specific prompt-gamma dose monitoring in proton therapy.

Online dose monitoring in proton therapy is currently being investigated with prompt-gamma (PG) devices. PG emission was shown to be correlated with dose deposition. This relationship is mostly unknown under real conditions. We propose a machine learning approach based on simulations to create optimized treatment-specific classifiers that detect discrepancies between planned and delivered dose. Simulations were performed with the Monte-Carlo platform Gate/Geant4 for a spot-scanning proton therapy treatment and a PG camera prototype currently under investigation. The method first builds a learning set of perturbed situations corresponding to a range of patient translation. This set is then used to train a combined classifier using distal falloff and registered correlation measures. Classifier performances were evaluated using receiver operating characteristic curves and maximum associated specificity and sensitivity. A leave-one-out study showed that it is possible to detect discrepancies of 5 mm with specificity and sensitivity of 85% whereas using only distal falloff decreases the sensitivity down to 77% on the same data set. The proposed method could help to evaluate performance and to optimize the design of PG monitoring devices. It is generic: other learning sets of deviations, other measures and other types of classifiers could be studied to potentially reach better performance. At the moment, the main limitation lies in the computation time needed to perform the simulations.