Correction: "Generation and comparison of 3D dosimetric reference datasets for COMS eye plaque brachytherapy using model-based dose calculations" https://doi.org/10.1002/mp.16721.

A joint Working Group of the American Association of Physicists in Medicine (AAPM), the European Society for Radiotherapy and Oncology (ESTRO), and the Australasian Brachytherapy Group (ABG) was created to aid in the transition from the AAPM TG-43 dose calculation formalism, the current standard, to model-based dose calculations. This work establishes the first test cases for low-energy photon-emitting brachytherapy using model-based dose calculation algorithms (MBDCAs). Five test cases are developed: (1) a single model 6711 125 I brachytherapy seed in water, 13 seeds (2) individually and (3) in combination in water, (4) the full Collaborative Ocular Melanoma Study (COMS) 16 mm eye plaque in water, and (5) the full plaque in a realistic eye phantom. Calculations are done with four Monte Carlo (MC) codes and a research version of a commercial treatment planning system (TPS). For all test cases, local agreement of MC codes was within ∼2.5% and global agreement was ∼2% (4% for test case 5). MC agreement was within expected uncertainties. Local agreement of TPS with MC was within 5% for test case 1 and ∼20% for test cases 4 and 5, and global agreement was within 0.4% for test case 1 and 10% for test cases 4 and 5. Dose distributions for each set of MC and TPS calculations are available online (https://doi.org/10.52519/00005) along with input files and all other information necessary to repeat the calculations. These data can be used to support commissioning of MBDCAs for low-energy brachytherapy as recommended by TGs 186 and 221 and AAPM Report 372. This work additionally lays out a sample framework for the development of test cases that can be extended to other applications beyond eye plaque brachytherapy.

Purpose: The TG‐186 report provides guidance to early adopters of model‐based dose calculation algorithms (MBDCAs) for brachytherapy. A charge of the AAPM‐ESTRO Working Group on MBDCA is to develop well‐defined test case plans, available as references for the software commissioning process to be performed by end‐users. The aim of this work is to develop test case plans for a generic HDR 192Ir source alone and in combination with a vaginal cylinder applicator with 180° tungsten‐alloy shielding in a DICOM‐based water phantom. Methods: A DICOM CT dataset was created with a 30 cm diameter water sphere surrounded by air. The voxel size was 1.33×1.33×1.33 mm3 for evaluating absorbed dose rate (cGy.h−1.U−1). Dose distributions were obtained in the planes x=0, y=0 and z=0. Three geometries were considered with the HDR source (a) at the center of sphere, (b) displaced 12.75 cm laterally, and (c) at the center of the applicator. Monte Carlo (MC) codes (Geant4, Penelope2009, MCNP5, and EGSnrc‐based BrachyDose) and Acuros were used. MC simulations were performed by multiple users to validate consistency of the DICOM reading, calculation set‐up, and dose reporting. Results: For all three geometries, MC results were generally within 2% agreement for r < 14 cm except at the sphere boundary where volume averaging occurred in voxels comprising two materials. There were no trends for r > 14 cm except that dosimetric agreement amongst the codes was limited by computational statistics. Results from Acuros and Penelope2009 were compared using a 2%/1.33 mm λ‐index criteria for r < 7 cm. Voxel pass rates were 100%, 100%, and 94% for geometries (a), (b), and (c), respectively. Conclusions: Dose rate distributions were consistent within statistical uncertainties among MC codes and for different users. Acuros reproduces MC data for the three geometries. These geometries may serve as test case plans for MBDCAs. T. Wareing is employee of Transpire Inc. that developed Acuros.

Traditional engineering-based core analyses generally rely on a two-stage approach, involving a transport code and a diffusion (e.g. nodal) solver. The accuracy and fidelity of the core solution strongly depend on the generation of few-group constants, which is a topic that has been extensively studied. However, the practice of calculating group constants with Monte Carlo (MC) codes is still relatively new. The main challenge of this approach is the generation of diffusion coefficients. A common approach adopted by many MC codes is the flux separability approximation (FSA). However, diffusion coefficients generated with FSA have trouble matching the heterogenous solution. This is due to a strong coupling between the energetic, spatial, and angular distributions of neutrons. This is a well-documented challenge, with some literature proposing consistent methods to handle energy-angle dependence and others proposing consistent methods to handle energy-space-angle dependence. Recently, the Cumulative Migration Method has been used to generate consistent diffusion coefficients using MC. However, these diffusion coefficients are valid only for infinite single region problems. As such, it is important to revisit the consistent group constant generation method to better handle the energy-space-angle dependence using MC. This paper will lay out the method to generate these consistent group constants using MC and then verify that they can then be used for a two-stage approach using a steady-state nodal expansion method.

Objective To investigate the impact of metal plate on radiation dose distribution by Monte Carlo (MC) code. Methods The metal plates with 0. 4 thick were placed in water at 5 cm, all the plate irradiated with 6 MV X-ray were simulated by MC code, SSD =100 cm. The percentage depth dose with or without metal implants were compared. Results The surface absorbed doses on incident plane of stainless steel plate and titanium plate were increased by 19. 6％ and 15.7％ respectively as compared water,the dose influence was less than 1.5％ more than 0. 3 cm outside the incidence plane. The doses on the exit surface of stainless steel plate and titanium plate were lowered by 8. 6％ and 8. 2％ when compared with water, the dose impacts of this places where were from the exit surface of stainless steel plate 、titanium plate more than 1.2 cm,0. 9 cm were less than 1.5％. The surface absorbed doses on incident plane of stainless steel plate were increased by 3.9％ respectively as titanium plate, the dose of the exit surface of stainless steel plate and titanium plate was similar. Conclusions MC method is a fast and accurate calculation method. The influence of metal plate on the radiotherapy dose distribution is significant. Under the such condition, the impact of stainless steel plate is much more than that of titanium alloy plate. Key words: Monte Carlo; Radiation dose; Metal implants

The ARPANSA calibration service for (60)Co gamma rays is based on a primary standard graphite calorimeter that measures absorbed dose to graphite. Measurements with the calorimeter are converted to the absorbed dose to water using the calculation of the ratio of the absorbed dose in the calorimeter to the absorbed dose in a water phantom. ARPANSA has recently changed the basis of this calculation from a photon fluence scaling method to a direct Monte Carlo (MC) calculation. The MC conversion uses an EGSnrc model of the cobalt source that has been validated against water tank and graphite phantom measurements, a step that is required to quantify uncertainties in the underlying interaction coefficients in the MC code. A comparison with the Bureau International des Poids et Mesures (BIPM) as part of the key comparison BIPM.RI(I)-K4 showed an agreement of 0.9973 (53).

BackgroundSeveral research groups have explored the potential of scandium radionuclides for theragnostic applications due to their longer half-lives and equal or similar coordination chemistry between their diagnostic and therapeutic counterparts, as well as lutetium-177 and terbium-161, respectively. Unlike the gallium-68/lutetium-177 pair, which may show different in-vivo uptake patterns, the use of scandium radioisotopes promises consistent behaviour between diagnostic and therapeutic radiopeptides. An advantage of scandium’s longer half-life over gallium-68 is the ability to study radiopeptide uptake over extended periods and its suitability for centralized production and distribution. However, concerns arise from scandium-44’s decay characteristics and scandium-43’s high production costs. This study aimed to evaluate the dosimetric implications of using scandium radioisotopes with somatostatin analogues against gallium-68 for PET imaging of neuroendocrine tumours.MethodsAbsorbed dose per injected activity (AD/IA) from the generated time-integrated activity curve (TIAC) were estimated using the radiopeptides [43/44/44mSc]Sc- and [68Ga]Ga-DOTATATE. The kidneys, liver, spleen, and red bone marrow (RBM) were selected for dose estimation studies. The EGSnrc and MCNP6.1 Monte Carlo (MC) codes were used with female (AF) and male (AM) ICRP phantoms. The results were compared to Olinda/EXM software, and the effective dose concentrations assessed, varying composition between the scandium radioisotopes.ResultsOur findings showed good agreement between the MC codes, with − 3 ± 8% mean difference. Kidneys, liver, and spleen showed differences between the MC codes (min and max) in a range of − 4% to 8%. This was observed for both phantoms for all radiopeptides used in the study. Compared to Olinda/EXM the largest observed difference was for the RBM, of 21% for the AF and 16% for the AM for scandium- and gallium-based radiopeptides. Despite the differences, our findings showed a higher absorbed dose on [43/44Sc]Sc-DOTATATE compared to its 68Ga-based counterpart.ConclusionThis study found that [43/44Sc]Sc-DOTATATE delivers a higher absorbed dose to organs at risk compared to [68Ga]Ga-DOTATATE, assuming equal distribution. This is due to the longer half-life of scandium radioisotopes compared to gallium-68. However, calculated doses are within acceptable ranges, making scandium radioisotopes a feasible replacement for gallium-68 in PET imaging, potentially offering enhanced diagnostic potential with later timepoint imaging.

This paper presents static and transient solutions for the pressurized water reactor (PWR) MOX/UO2 transient benchmark by Serpent 2 Monte Carlo (MC) code and open nodal core simulator called Abu Dhabi Polytechnic Reactor Simulator (ADPRES). The presence of mixed oxide (MOX) fuels and burn-up variation in the benchmark pose challenges for reactor simulators due to severe flux gradient across fuel assemblies. In this work, the two-step method was used, in which the assembly level two-group constants were generated from single assembly calculations with zero net current boundary conditions using Serpent 2 Monte Carlo code, and later the core calculation was performed using ADPRES open nodal core simulator. Two types of diffusion coefficients were generated: the conventional B1 leakage corrected and the cumulative migration method (CMM). Finally, the solutions of Serpent 2/ADPRES, including multiplication factor, power distribution, control rod (CR) worth, and critical boron concentration (CBC) using both diffusion coefficients, were compared against solutions from heterogeneous Serpent 2 calculations where the fuel and cladding are explicitly modeled. The reactor power during transients was also compared qualitatively against other nodal core simulators. The results showed that Serpent 2/ADPRES were able to predict the heterogeneous Monte Carlo solutions very well with reasonable differences. The transient solutions were also quite accurate compared to other nodal core simulators. For the diffusion coefficients comparison, it was found that the CMM diffusion coefficient provides more accurate solutions for the benchmark compared to the B1 leakage corrected diffusion coefficients.

Optimization treatment of point-wise nuclear data in Monte Carlo criticality and burnup calculations

Monte Carlo (MC) methods are recognized as the gold-standard for dose calculation, however they have not replaced analytical methods up to now due to their lengthy calculation times. GPU-based applications allow MC dose calculations to be performed on time scales comparable to conventional analytical algorithms. This study focuses on validating our GPU-based MC code for proton dose calculation (gPMC) using an experimentally validated multi-purpose MC code (TOPAS) and compare their performance for clinical patient cases. Clinical cases from five treatment sites were selected covering the full range from very homogeneous patient geometries (liver) to patients with high geometrical complexity (air cavities and density heterogeneities in head-and-neck and lung patients) and from short beam range (breast) to large beam range (prostate). Both gPMC and TOPAS were used to calculate 3D dose distributions for all patients. Comparisons were performed based on target coverage indices (mean dose, V95, D98, D50, D02) and gamma index distributions. Dosimetric indices differed less than 2% between TOPAS and gPMC dose distributions for most cases. Gamma index analysis with 1%/1 mm criterion resulted in a passing rate of more than 94% of all patient voxels receiving more than 10% of the mean target dose, for all patients except for prostate cases. Although clinically insignificant, gPMC resulted in systematic underestimation of target dose for prostate cases by 1–2% compared to TOPAS. Correspondingly the gamma index analysis with 1%/1 mm criterion failed for most beams for this site, while for 2%/1 mm criterion passing rates of more than 94.6% of all patient voxels were observed. For the same initial number of simulated particles, calculation time for a single beam for a typical head and neck patient plan decreased from 4 CPU hours per million particles (2.8–2.9 GHz Intel X5600) for TOPAS to 2.4 s per million particles (NVIDIA TESLA C2075) for gPMC. Excellent agreement was demonstrated between our fast GPU-based MC code (gPMC) and a previously extensively validated multi-purpose MC code (TOPAS) for a comprehensive set of clinical patient cases. This shows that MC dose calculations in proton therapy can be performed on time scales comparable to analytical algorithms with accuracy comparable to state-of-the-art CPU-based MC codes.

The Constructive Solid Geometry (CSG) allows modelers to create models by using Boolean operations to combine different types of basic solids. CSG has been adopted by lots of numerical simulation codes as geometric description. In the research of reactor numerical simulation, Monte Carlo (MC) is a common method for neutronics analysis, which needs to calculate the intersections between the random flight paths of neutrons and the geometry. A lot of widely used MC codes (such as Geant4, MCNP) use CSG to describe geometry. The prerequisite to run MC code is to do geometric modeling, which however in many MC codes, is finished by manually writing text files, it is very hard to view the model and it is inefficient to build. In this work, a MC modeling tool (JLAMT) based on UG software is used to build the benchmark model. The tool supplies geometric modeling functions, physical parameter setting functions, and other assistant tools. With a flexible GUI, it helps users to achieve quick modeling.

The nucleation of sulfuric acid-water clusters plays a significant role in the formation of aerosols. Based on a recently developed particle Monte Carlo (MC) Code, we analyze how the growth of sulfuric acid-water clusters is influenced by stochastic fluctuations. We here consider samples of H2SO4-H2O clusters at T = 200 K with a relative humidity of 50%, with particle concentrations between 105 and 107 cm–3 in volumes between 10-6 and 10-2 cm3. We present the temporal evolution of the formation rate and of the size distribution as well as growth rates and the onset time of the nucleation above a given cluster size with and without constant production of new monomers. Clear evidence is revealed by the MC code that fluctuations result in a faster growth rate of the smallest clusters compared to deterministic continuum models that do not contain the stochastic effects. The faster growth of small clusters in turn influences the growth of larger clusters. Depending on the volume size, the onset time for clusters larger than 0.85 nm varies between 1000 s and 20,000 s for n=105 cm–3 and between 10 s and 100 s for n=107 cm–3.

Purpose: Monte Carlo (MC) simulation provides the most accurate results for CT dose calculations. Yet it is a time‐consuming task which often takes long time to run on CPUs. Using the recently developed hardware accelerators such as Graphics Processing Unit (GPU) and the Intel Xeon Phi coprocessor, one can run the MC simulations in massively parallel and reduce the simulation time significantly. In this work we present our effort of developing a new MC suite, Accelerated Radiation‐transport Computations in Heterogeneous EnviRonments (ARCHER), which utilizes NVIDIA GPUs and other accelerators. Methods: We first developed the radiation transport MC codes in C, and then adapted the code to ARCHERGPU and ARCHERMIC which are specific for NVIDIA GPU and Intel MIC architecture, respectively. Currently the code is capable of calculating the dose for CT X‐ray scan and electron radiotherapy. Our code also incorporated a whole CT scanner model and various computational human phantoms. Results: We tested our GPU‐based MC code on NVIDIA Tesla M2090 GPUs and compared its performance with other MC codes running on an Intel Xeon X5650 CPU. Two tests were carried out: 1) For a whole body helical CT scan with RPI‐Adult male obese phantom and 9e8 incoming photons, the ARCHER_GPU code and ARCHER_MIC code are 1200 and 561 times faster, respectively, than MCNPX running on the CPU. 2) For a simple water‐aluminum‐water phantom and 20MeV electron source, the ARCHER_GPU code takes 186 sec to simulate 6e6 electrons, and it is about 1000 times faster than MCNPX running on the CPU. Conclusion: Our preliminary results showed the efficiency of using different hardware accelerators to speed up MC radiation transport simulations for medical dosimetry. On‐going work includes further optimization of the code and implementation of more complex physics models.

Purpose:To describe a new infrastructure and process that will enable end‐users to commission model‐based dose calculation (MBDC) software for brachytherapy planning, and to invite end‐users to participate in the beta testing phase of this process.Methods:The AAPM working groups on model‐based dose calculation algorithms in brachytherapy (WGDCAB) and brachytherapy source registry (WGBSR) collaboratively implemented the basic MBDC software commissioning workflow described in the AAPM+ESTRO TG‐186 report. For a small number of initial test cases virtual CT image, anatomical structure set, treatment plan, and associated 3D dose data for a single dwell position occupied by a WGDCAB generic high‐dose‐rate Ir‐192 virtual source, with or without a generic shielded applicator, were generated. Dose distributions were calculated using Monte Carlo (MC) code MCNP6, Elekta's Advanced Collapsed cone Algorithm (ACE™), and Varian's Acuros™ BV module. All data were saved in DICOM RT format and uploaded to a provisional, web‐accessible repository in the joint AAPM—IROC Houston brachytherapy source registry. It is envisioned that end‐users will download data for a test case from the repository, verify its correctness, calculate 3D dose locally, and then compare with MCNP6 and MBDC doses obtained from the repository using TPS‐based comparison tools. User guides in preparation for each TPS will facilitate the process.Results:Initial evaluations of DICOM data download, TPS import, MBDC dose calculation, and reference dose comparison indicate that the infrastructure and process described above are both viable and practicable. Ancillary data including generic source and shielded applicator specifications, CAD drawings, and MC code definitions are also available in the repository.Conclusion:With further refinement and beta‐test input from end‐users, a practical MBDC software commissioning process for brachytherapy can be established. A procedure for adding new test cases submitted by end‐users to the repository will be put in place by WGDCAB.

COMS eye plaque brachytherapy dosimetric sensitivity to source photon energy and seed design

Purpose: To investigate the dosimetric optimization methods of varied intra‐plaque seed strength and radionuclide choice for COMS eye plaque brachytherapy. Methods: The COMS plaque Silastic insert arranges seeds about concentric circles (rings) of different radii. Thus, each plaque dose distribution may be considered as the superposition of its constituent rings. The MCNP5 radiation transport code was used to simulate a 16mm COMS plaque populated with 103Pd, 125I, and 131Cs sources. Each constituent source ring was then activated to determine its contribution to the total dose distribution while accounting for the dosimetric influences of inter‐seed attenuation and scatter. Tumor dose distributions were generated from >106 permutations of ring weightings and radionuclides for a given apical prescription dose. These dose distributions were analyzed for target dose uniformity and conformity, and compared to that of a uniformly‐loaded 125I plaque. Results: Using 131Cs seeds in only the plaque‘s outer ring yielded the most homogeneous dose distribution (+16% vs. 125I). The proximal sclera dose was 42% lower, yet center of the eye and opposite retina doses were 16% and 30% higher, respectively. Combinations with 103Pd in the plaque inner ring weighted to deliver >98% of the prescription dose yielded the most conformal dose distributions (>24% vs. 12 5I). Though doses to the eye center and opposite retina were >32% and >55% lower, respectively, the proximal sclera dose was >26% higher. Various combinations using only 103Pd with the outer ring contributing >50% of the prescription dose produced dose distributions both more homogeneous and more conformal than 125I. Proximal sclera, eye center, and opposite retina doses were typically 8‐12%, 11‐14%, and 38–40% lower, respectively, than using 125I. Conclusions: Intra‐plaque radionuclide and seed strength variations produced more homogeneous and conformal dose distributions than uniformly‐loaded COMS plaques. This optimization approach is generalizable to all plaque sizes and tumor dimensions.