The impact of the titanium cranial hardware in proton single-field uniform dose plans.

BACKGROUND: Neurosurgical cranial titanium mesh and screws are commonly encountered in postoperative radiation therapy. However, only a limited number of reports are available in the context of proton therapy, resulting in a lack of consensus among the proton centers regarding the protocol for handling the hardware. PURPOSE: This study is to examine the impact of the hardware in proton plans. The results serve as evidence for proton centers to generate standard operating procedures to manage the hardware in proton treatment. METHODS: Plans with different gantry angles and material overrides are generated on the CT images of a phantom made of the hardware. The dose distributions of the plans with and without material override, at different depths are compared. Films and ionization chambers are used to measure the plans and the measurements are compared to the treatment planning system (TPS) calculations by gamma analysis. RESULTS: There are some overdose and underdose regions downstream of the hardware. The overdose and underdose values are within a few percent of the prescribed dose when multiple fields with large hinge angles are used. The gamma analysis results show that the measurements agree with the TPS calculations within limits that are clinically relevant. CONCLUSION: The study has demonstrated the influence of the hardware on proton plans. Based on the result of this study, a standard operating procedure of managing the hardware has been implemented in our clinic.

A component method to delineate surgical spine implants for proton Monte Carlo dose calculation.

PURPOSE: Metallic implants have been correlated to local control failure for spinal sarcoma and chordoma patients due to the uncertainty of implant delineation from computed tomography (CT). Such uncertainty can compromise the proton Monte Carlo dose calculation (MCDC) accuracy. A component method is proposed to determine the dimension and volume of the implants from CT images. METHODS: The proposed component method leverages the knowledge of surgical implants from medical supply vendors to predefine accurate contours for each implant component, including tulips, screw bodies, lockers, and rods. A retrospective patient study was conducted to demonstrate the feasibility of the method. The reference implant materials and samples were collected from patient medical records and vendors, Medtronic and NuVasive. Additional CT images with extensive features, such as extended Hounsfield units and various reconstruction diameters, were used to quantify the uncertainty of implant contours. RESULTS: For in vivo patient implant estimation, the reference and the component method differences were 0.35, 0.17, and 0.04 cm3 for tulips, screw bodies, and rods, respectively. The discrepancies by a conventional threshold method were 5.46, 0.76, and 0.05 cm3 , respectively. The mischaracterization of implant materials and dimensions can underdose the clinical target volume coverage by 20 cm3 for a patient with eight lumbar implants. The tulip dominates the dosimetry uncertainty as it can be made from titanium or cobalt-chromium alloys by different vendors. CONCLUSIONS: A component method was developed and demonstrated using phantom and patient studies with implants. The proposed method provides more accurate implant characterization for proton MCDC and can potentially enhance the treatment quality for proton therapy. The current proof-of-concept study is limited to the implant characterization for lumbar spine. Future investigations could be extended to cervical spine and dental implants for head-and-neck patients where tight margins are required to spare organs at risk.

Reconstructive dosimetry and radiation dose evaluation of workers and public due to a Brazilian radiological accident in industrial radiography.

Radiological accidents occur mainly in the practices recognized as high risk and which are classified by the International Atomic Energy Agency (IAEA) as Categories 1 and 2: radiotherapy, industrial irradiators and industrial radiography. In Brazil, five important cases in industrial gamma radiography occurred from 1985 to 2018, involving seven radiation workers and 19 members of the public. The accidents caused localized radiation lesions on the hands and fingers. One of these accidents is the focus of this paper. In this accident, a 3.28 TBq192Ir radioactive source was left unshielded for 9 h in a non-destructive testing (NDT) company parking lot, and many radiation workers, employees and public, including teachers of a primary school were exposed. The radioactive source was also directly handled by a security worker for about 1.5 min causing severe radiation injuries in the hand and fingers. This paper presents radiation dose estimates for all accidentally exposed individuals. Four scenarios were considered, and three internationally recognised and updated reconstructive dosimetry techniques were used, named, Brazilian visual Monte Carlo Dose Calculation (VMC), virtual environment for radiological and nuclear accidents simulation (AVSAR) and RADPRO Calculator®. The main radiation doses estimated by VMC were the absorbed dose of 34 Gy for the security worker's finger and his effective dose of 91 mSv; effective doses from 43 to 160 mSv for radiation workers and NDT employees; and effective doses of 9 mSv for teachers in the schoolyard.

Detailed tooth models for ICRP mesh-type reference computational phantoms.

For use in electron paramagnetic resonance dosimetry with tooth enamel, in the present study, very detailed mesh-type tooth models composed of 198 individual tooth models (i.e. newborn: 20; 1 year: 28; 5 years: 48; 10 years: 38; 15 years: 32; and adult: 32) were developed for each sex. The developed tooth models were then implanted in the International Commission on Radiological Protection pediatric and adult mesh-type reference computational phantoms and used to calculate tooth enamel doses, by Monte Carlo simulations with Geant4, for external photon exposures in several idealized irradiation geometries. The calculated dose values were then compared to investigate the dependency of the enamel dose on the age and sex of the phantom and the sites of the teeth. The results of the present study generally show that, if the photon energy is low (i.e. <0.1 MeV), the enamel dose is significantly affected by the age and sex of the phantom and also the sites of the teeth used for dose calculation; the differences are frequently greater than a few times or even orders of magnitude. However, with a few exceptions, the enamel dose was hardly affected by these parameters for energies between 0.1 and 3 MeV. For energies >3 MeV, moderate differences were observed (i.e., up to a factor of two), due to the existence of dose build-up in the head of the phantom for high-energy photons. The calculated dose values were also compared with those of the previous studies where voxel and mathematical models were used to calculate the enamel doses. The results again show significant differences at low energies, e.g., up to ∼3500 times at 0.015 MeV, which are mainly due to the differences in the level of tooth-modeling detailedness.

A benchmarking method to evaluate the accuracy of a commercial proton monte carlo pencil beam scanning treatment planning system.

AcurosPT is a Monte Carlo algorithm in the Eclipse 13.7 treatment planning system, which is designed to provide rapid and accurate dose calculations for proton therapy. Computational run-time in minimized by simplifying or eliminating less significant physics processes. In this article, the accuracy of AcurosPT was benchmarked against both measurement and an independent MC calculation, TOPAS. Such a method can be applied to any new MC calculation for the detection of potential inaccuracies. To validate multiple Coulomb scattering (MCS) which affects primary beam broadening, single spot profiles in a Solidwater® phantom were compared for beams of five selected proton energies between AcurosPT, measurement and TOPAS. The spot Gaussian sigma in AcurosPT was found to increase faster with depth than both measurement and TOPAS, suggesting that the MCS algorithm in AcurosPT overestimates the scattering effect. To validate AcurosPT modeling of the halo component beyond primary beam broadening, field size factors (FSF) were compared for multi-spot profiles measured in a water phantom. The FSF for small field sizes were found to disagree with measurement, with the disagreement increasing with depth. Conversely, TOPAS simulations of the same FSF consistently agreed with measurement to within 1.5%. The disagreement in absolute dose between AcurosPT and measurement was smaller than 2% at the mid-range depth of multi-energy beams. While AcurosPT calculates acceptable dose distributions for typical clinical beams, users are cautioned of potentially larger errors at distal depths due to overestimated MCS and halo implementation.

Moving GPU-OpenCL-based Monte Carlo dose calculation toward clinical use: Automatic beam commissioning and source sampling for treatment plan dose calculation.

We have previously developed a GPU-based Monte Carlo (MC) dose engine on the OpenCL platform, named goMC, with a built-in analytical linear accelerator (linac) beam model. In this paper, we report our recent improvement on goMC to move it toward clinical use. First, we have adapted a previously developed automatic beam commissioning approach to our beam model. The commissioning was conducted through an optimization process, minimizing the discrepancies between calculated dose and measurement. We successfully commissioned six beam models built for Varian TrueBeam linac photon beams, including four beams of different energies (6 MV, 10 MV, 15 MV, and 18 MV) and two flattening-filter-free (FFF) beams of 6 MV and 10 MV. Second, to facilitate the use of goMC for treatment plan dose calculations, we have developed an efficient source particle sampling strategy. It uses the pre-generated fluence maps (FMs) to bias the sampling of the control point for source particles already sampled from our beam model. It could effectively reduce the number of source particles required to reach a statistical uncertainty level in the calculated dose, as compared to the conventional FM weighting method. For a head-and-neck patient treated with volumetric modulated arc therapy (VMAT), a reduction factor of ~2.8 was achieved, accelerating dose calculation from 150.9 s to 51.5 s. The overall accuracy of goMC was investigated on a VMAT prostate patient case treated with 10 MV FFF beam. 3D gamma index test was conducted to evaluate the discrepancy between our calculated dose and the dose calculated in Varian Eclipse treatment planning system. The passing rate was 99.82% for 2%/2 mm criterion and 95.71% for 1%/1 mm criterion. Our studies have demonstrated the effectiveness and feasibility of our auto-commissioning approach and new source sampling strategy for fast and accurate MC dose calculations for treatment plans.

Impact on the estimated dose of different tissue assignment strategies during partial breast irradiations with INTRABEAM.

PURPOSE: Partial breast irradiations with electronic brachytherapy or kilovoltage intraoperative radiotherapy devices such as Axxent or INTRABEAM are becoming more common every day. Breast is mainly composed of glandular and adipose tissues, which are not always clearly disentangled in planning breast CTs. In these cases, breast tissues are replaced with an average soft tissue, or even water. However, at kilovoltage energies, this may lead to large differences in the delivered dose, due to the dominance of photoelectric effect. Therefore, the aim of this work was to study the effect on the dose prescribed in breast with the INTRABEAM device using different soft tissue assignment strategies that would replace the adipose and glandular tissues that constitute the breast in cases where these tissues cannot be adequately distinguished in a CT scan. METHODS AND MATERIALS: Dose was computed with a Monte Carlo code in five patients with a 3 cm diameter INTRABEAM spherical applicator. Tissues within the breast were assigned following six different strategies: one based on the TG-43 recommendations, representing the whole breast as water of unity density, another one also water-based but with CT derived density, and the other four also based on CT-derived densities, using a single tissue resulting from different mixes of glandular and adipose tissues. These were compared against the reference dose computed in an accurately segmented CT, following TG-186 recommendations. Relative differences and dose ratios between the reference and the other tissue assignment strategies were obtained in three regions of interest inside the breast. RESULTS AND CONCLUSIONS: Dose planning in water-based tissues was found inaccurate for breast treatment with INTRABEAM, as it would incur in up to 30% under-prescription of dose. If accurate soft tissue assignments in the breast cannot be safely done, a single-tissue composition of 80% adipose and 20% glandular tissue, or even a 100% adipose tissue, would be recommended to avoid dose under-prescription.

A framework for in-field and out-of-field patient specific secondary cancer risk estimates from treatment plans using the TOPAS Monte Carlo system.

Objective. To allow the estimation of secondary cancer risks from radiation therapy treatment plans in a comprehensive and user-friendly Monte Carlo (MC) framework.Method. Patient planning computed tomography scans were extended superior-inferior using the International Commission on Radiological Protection's Publication 145 computational mesh phantoms and skeletal matching. Dose distributions were calculated with the TOPAS MC system using novel mesh capabilities and the digital imaging and communications in medicine radiotherapy extension interface. Finally, in-field and out-of-field cancer risk was calculated using both sarcoma and carcinoma risk models with two alternative parameter sets.Result. The TOPAS MC framework was extended to facilitate epidemiological studies on radiation-induced cancer risk. The framework is efficient and allows automated analysis of large datasets. Out-of-field organ dose was small compared to in-field dose, but the risk estimates indicate a non-negligible contribution to the total radiation induced cancer risk.Significance. This work equips the TOPAS MC system with anatomical extension, mesh geometry, and cancer risk model capabilities that make state-of-the-art out-of-field dose calculation and risk estimation accessible to a large pool of users. Furthermore, these capabilities will facilitate further refinement of risk models and sensitivity analysis of patient specific treatment options.

Treatment planning of scanned proton beams in RayStation.

The use of scanned proton beams in external beam radiation therapy has seen a rapid development over the past decade. This technique places new demands on treatment planning, as compared to conventional photon-based radiation therapy. In this article, several proton specific functions as implemented in the treatment planning system RayStation are presented. We will cover algorithms for energy layer and spot selection, basic optimization including the handling of spot weight limits, optimization of the linear energy transfer (LET) distribution, robust optimization including the special case of 4D optimization, proton arc planning, and automatic planning using deep learning. We will further present the Monte Carlo (MC) proton dose engine in RayStation to some detail, from the material interpretation of the CT data, through the beam model parameterization, to the actual MC transport mechanism. Useful tools for plan evaluation, including robustness evaluation, and the versatile scripting interface are also described. The overall aim of the paper is to give an overview of some of the key proton planning functions in RayStation, with example usages, and at the same time provide the details about the underlying algorithms that previously have not been fully publicly available.

Investigation of four-dimensional (4D) Monte Carlo dose calculation in real-time tumor tracking stereotatic body radiotherapy for lung cancers.

PURPOSE: To investigate the dosimetric variations and radiobiological impacts as a consequence of delivering treatment plans of 3D nature in 4D manner based on the 4D Monte Carlo treatment planning framework implemented on Cyberknife. METHODS AND MATERIALS: Dose distributions were optimized on reference 3D images at end of exhale phase of a 4DCT dataset for twenty-five lung cancer patients treated with 60 Gy / 3Fx or 48 Gy / 4Fx. Deformable image registrations (DIR) between individual 3DCT images to the reference 3DCT image in the 4DCT study were performed to interpolate doses calculated on multiple anatomical geometries back on to the reference geometry to compose a 4D dose distribution that included the tracking beam motion and organ deformation. The 3D and 4D dose distributions that were initially calculated with the equivalent path-length (EPL) algorithm (3DEPL dose and 4DEPL dose) were recalculated with the Monte Carlo algorithm (3DMC dose and 4DMC dose). Dosimetric variations of V60Gy / 48Gy and D99 of GTV, mean doses to the lung and the heart and maximum dose (D1 ) of the spinal cord as a consequence of tracking beam motion in deforming anatomy, dose calculation algorithm, and both were quantified by the relative change from 4DMC to 3DMC doses, from 4DMC to 4DEPL doses, and from 4DMC to 3DEPL doses, respectively. RESULTS: Comparing 4DMC to 3DEPL plans, V60Gy / 48Gy and D99 of GTV decreased considerably by 13 ± 22% (mean ± 1SD) and 9.2 ± 5.5 Gy but changes of normal tissue doses were not more than 0.5 Gy on average. The generalized equivalent uniform dose (gEUD) and tumor control probability (TCP) were reduced by 14.3 ± 8.8 Gy and 7.5 ± 5.2%, and normal tissue complication probability (NTCP) for myelopathy and pericarditis were close to zero and NTCP for radiation pneumonitis was reduced by 2.5 ± 4.1%. Comparing 4DMC to 4DEPL plans found decreased V60Gy / 48Gy and D99 by 12.3 ± 21.6% and 7.3 ± 5.3 Gy, the normal tissues doses by 0.5 Gy on average, gEUD and TCP by 13.0 ± 8.6 Gy and 7.1 ± 5.1%. Comparing 4DMC to 3DMC doses, V60Gy / 48Gy and D99 of GTV was reduced by 5.2 ± 8.8 %and 2.6 ± 3.3 Gy, and normal tissues hardly changed from 4DMC to 3DMC doses. The corresponding decreases of gEUD and TCP were 2.8 ± 4.0 Gy and 1.6 ± 2.4%. CONCLUSION: The large discrepancy between original 3DEPL plan and benchmarking 4DMC plan is predominately due to dose calculation algorithms as the tracking beam motion and organ deformation hardly influenced doses of normal tissues and moderately decreased V60Gy / 48Gy and D99 of GTV. It is worth to make a thoughtful weight of the benefits of full 4D MC dose calculation and consider 3D MC dose calculation as a compromise of 4D MC dose calculation considering the multifold computation time. This article is protected by copyright. All rights reserved.

Validation of a deep learning-based material estimation model for Monte Carlo dose calculation in proton therapy.

Objective. Computed tomography (CT) to material property conversion dominates proton range uncertainty, impacting the quality of proton treatment planning. Physics-based and machine learning-based methods have been investigated to leverage dual-energy CT (DECT) to predict proton ranges. Recent development includes physics-informed deep learning (DL) for material property inference. This paper aims to develop a framework to validate Monte Carlo dose calculation (MCDC) using CT-based material characterization models.Approach.The proposed framework includes two experiments to validatein vivodose and water equivalent thickness (WET) distributions using anthropomorphic and porcine phantoms. Phantoms were irradiated using anteroposterior proton beams, and the exit doses and residual ranges were measured by MatriXX PT and a multi-layer strip ionization chamber. Two pre-trained conventional and physics-informed residual networks (RN/PRN) were used for mass density inference from DECT. Additional two heuristic material conversion models using single-energy CT (SECT) and DECT were implemented for comparisons. The gamma index was used for dose comparisons with criteria of 3%/3 mm (10% dose threshold).Main results. The phantom study showed that MCDC with PRN achieved mean gamma passing rates of 95.9% and 97.8% for the anthropomorphic and porcine phantoms. The rates were 86.0% and 79.7% for MCDC with the empirical DECT model. WET analyses indicated that the mean WET variations between measurement and simulation were -1.66 mm, -2.48 mm, and -0.06 mm for MCDC using a Hounsfield look-up table with SECT and empirical and PRN models with DECT. Validation experiments indicated that MCDC with PRN achieved consistent dose and WET distributions with measurement.Significance. The proposed framework can be used to identify the optimal CT-based material characterization model for MCDC to improve proton range uncertainty. The framework can systematically verify the accuracy of proton treatment planning, and it can potentially be implemented in the treatment room to be instrumental in online adaptive treatment planning.

Statistical breathing curve sampling to quantify interplay effects of moving lung tumors in a 4D Monte Carlo dose calculation framework.

PURPOSE: The interplay between respiratory tumor motion and dose application by intensity modulated radiotherapy (IMRT) techniques can potentially lead to undesirable and non-intuitive deviations from the planned dose distribution. We developed a 4D Monte Carlo (MC) dose recalculation framework featuring statistical breathing curve sampling, to precisely simulate the dose distribution for moving target volumes aiming at a comprehensive assessment of interplay effects. METHODS: We implemented a dose accumulation tool that enables dose recalculations of arbitrary breathing curves including the actual breathing curve of the patient. This MC dose recalculation framework is based on linac log-files, facilitating a high temporal resolution up to 0.1 s. By statistical analysis of 128 different breathing curves, interplay susceptibility of different treatment parameters was evaluated for an exemplary patient case. To facilitate prospective clinical application in the treatment planning stage, in which patient breathing curves or linac log-files are not available, we derived a log-file free version with breathing curves generated by a random walk approach. Interplay was quantified by standard deviations σ in D5%, D50% and D95%. RESULTS: Interplay induced dose deviations for single fractions were observed and evaluated for IMRT and volumetric arc therapy (σD95% up to 1.3 %) showing a decrease with higher fraction doses and an increase with higher MU rates. Interplay effects for conformal treatment techniques were negligible (σ<0.1%). The log-file free version and the random walk generated breathing curves yielded similar results (deviations in σ< 0.1 %) and can be used as substitutes for interplay assessment. CONCLUSION: It is feasible to combine statistically sampled breathing curves with MC dose calculations. The universality of the presented framework allows comprehensive assessment of interplay effects in retrospective and prospective clinically relevant scenarios.

A novel multichannel deep learning model for fast denoising of Monte Carlo dose calculations: preclinical applications.

Objective.In preclinical radiotherapy with kilovolt (kV) x-ray beams, accurate treatment planning is needed to improve the translation potential to clinical trials. Monte Carlo based radiation transport simulations are the gold standard to calculate the absorbed dose distribution in external beam radiotherapy. However, these simulations are notorious for their long computation time, causing a bottleneck in the workflow. Previous studies have used deep learning models to speed up these simulations for clinical megavolt (MV) beams. For kV beams, dose distributions are more affected by tissue type than for MV beams, leading to steep dose gradients. This study aims to speed up preclinical kV dose simulations by proposing a novel deep learning pipeline.Approach.A deep learning model is proposed that denoises low precision (∼106simulated particles) dose distributions to produce high precision (109simulated particles) dose distributions. To effectively denoise the steep dose gradients in preclinical kV dose distributions, the model uses the novel approach to use the low precision Monte Carlo dose calculation as well as the Monte Carlo uncertainty (MCU) map and the mass density map as additional input channels. The model was trained on a large synthetic dataset and tested on a real dataset with a different data distribution. To keep model inference time to a minimum, a novel method for inference optimization was developed as well.Main results.The proposed model provides dose distributions which achieve a median gamma pass rate (3%/0.3 mm) of 98% with a lower bound of 95% when compared to the high precision Monte Carlo dose distributions from the test set, which represents a different dataset distribution than the training set. Using the proposed model together with the novel inference optimization method, the total computation time was reduced from approximately 45 min to less than six seconds on average.Significance.This study presents the first model that can denoise preclinical kV instead of clinical MV Monte Carlo dose distributions. This was achieved by using the MCU and mass density maps as additional model inputs. Additionally, this study shows that training such a model on a synthetic dataset is not only a viable option, but even increases the generalization of the model compared to training on real data due to the sheer size and variety of the synthetic dataset. The application of this model will enable speeding up treatment plan optimization in the preclinical workflow.

Incorporation of micro-CT-based detailed bone models into ICRP adult mesh-type reference computational phantoms.

Objective.The red bone marrow (RBM) and bone endosteum (BE), which are required for effective dose calculation, are macroscopically modeled in the reference phantoms of the international commission on radiological protection (ICRP) due to their microscopic and complex histology. In the present study, the detailed bone models were developed to simplify the dose calculation process for skeletal dosimetry.Approach.The detailed bone models were developed based on the bone models developed at the University of Florida. A new method was used to update the definition of BE region by storing the BE location indices using virtual sub-voxels. The detailed bone models were then installed in the spongiosa regions of the ICRP mesh-type reference computational phantoms (MRCPs) via the parallel geometry feature of the Geant4 code.Main results.Comparing the results between the detailed-bone-installed MRCPs and the original MRCPs with the absorbed dose to spongiosa and fluence-to-dose response function (DRF)-based methods, the DRF-based method showed much smaller but still significant differences. Compared with the values given in ICRPPublications116 and 133, the differences were very large (i.e. several orders of magnitudes), due mainly to the anatomical improvement of the skeletal system in the MRCPs; that is, spongiosa and medullary cavity are fully enclosed by cortical bone in the MRCPs but not in the ICRP-110 phantoms.Significance.The detailed bone models enable the direct calculation of the absorbed doses to the RBM and BE, simplifying the dose calculation process and potentially improving the consistency and accuracy of skeletal dosimetry.

Monte Carlo-based software for 3D personalized dose calculations in image-guided radiotherapy.

BACKGROUND AND PURPOSE: Image-guided radiotherapy (IGRT) involves frequent in-room imaging sessions contributing to additional patient irradiation. The present work provided patient-specific dosimetric data related to different imaging protocols and anatomical sites. MATERIAL AND METHODS: We developed a Monte Carlo based software able to calculate 3D personalized dose distributions for five imaging devices delivering kV-CBCT (Elekta and Varian linacs), MV-CT (Tomotherapy machines) and 2D-kV stereoscopic images from BrainLab and Accuray. Our study reported the dose distributions calculated for pelvis, head and neck and breast cases based on dose volume histograms for several organs at risk. RESULTS: 2D-kV imaging provided the minimum dose with less than 1 mGy per image pair. For a single kV-CBCT and MV-CT, median dose to organs were respectively around 30 mGy and 15 mGy for the pelvis, around 7 mGy and 10 mGy for the head and neck and around 5 mGy and 15 mGy for the breast. While MV-CT dose varied sparsely with tissues, dose from kV imaging was around 1.7 times higher in bones than in soft tissue. Daily kV-CBCT along 40 sessions of prostate radiotherapy delivered up to 3.5 Gy to the femoral heads. The dose level for head and neck and breast appeared to be lower than 0.4 Gy for every organ in case of a daily imaging session. CONCLUSIONS: This study showed the dosimetric impact of IGRT procedures. Acquisition parameters should therefore be chosen wisely depending on the clinical purposes and tailored to morphology. Indeed, imaging dose could be reduced up to a factor 10 with optimized protocols.

Validation of histogram-based virtual source models for different IGRT kV-imaging systems.

PURPOSE: Image-guided radiotherapy (IGRT) improves tumor control but its intensive use may entrain late side effects caused by the additional imaging doses. There is a need to better quantify the additional imaging doses, so they can be integrated in the therapeutic workflow. Currently, no dedicated software enables to compute patient-specific imaging doses on a wide range of systems and protocols. As a first step toward this objective, we propose a common methodology to model four different kV-imaging systems used in radiotherapy (Varian's OBI, Elekta's XVI, Brainlab's ExacTrac, and Accuray's Cyberknife) using a new type of virtual source model based on Monte Carlo calculations. METHODS: We first describe our method to build a simplified description of the photon output, or virtual source models (VSMs), of each imaging system. Instead of being constructed using measurement data, as it is most commonly the case, our VSM is used as the summary of the phase-space files (PSFs) resulting from a first Monte Carlo simulation of the considered x-ray tube. Second, the VSM is used as a photon generator for a second MC simulation in which we compute the dose. Then, the proposed VSM is thoroughly validated against standard MC simulation using PSFs on the XVI system. Last, each modeled system is compared to profiles and depth-dose-curve measurements performed in homogeneous phantom. RESULTS: Comparisons between PSF-based and VSM-based calculations highlight that VSMs could provide equivalent dose results (within 1% of difference) than PSFs inside the imaging field-of-view (FOV). In contrast, VSMs tend to underestimate (for up to 20%) calculated doses outside of the imaging FOV due to the assumptions underlying the VSM construction. In addition, we showed that the use of VSMs allows reducing calculation time by at least a factor of 2.8. Indeed, for identical simulation times, statistical uncertainties on dose distributions computed using VSMs were much lower than those obtained from PSF-based calculations. CONCLUSIONS: For each of the four imaging systems, VSMs were successfully validated against measurements in homogeneous phantoms, and are therefore ready to be used for future preclinical studies in heterogeneous or anthropomorphic phantoms. The cross system modeling methodology developed here should enable, later on, to estimate precisely and accurately patient-specific 3D dose maps delivered during a large range of kV-imaging procedures.

Beam modeling and beam model commissioning for Monte Carlo dose calculation-based radiation therapy treatment planning: Report of AAPM Task Group 157.

Dose calculation plays an important role in the accuracy of radiotherapy treatment planning and beam delivery. The Monte Carlo (MC) method is capable of achieving the highest accuracy in radiotherapy dose calculation and has been implemented in many commercial systems for radiotherapy treatment planning. The objective of this task group was to assist clinical physicists with the potentially complex task of acceptance testing and commissioning MC-based treatment planning systems (TPS) for photon and electron beam dose calculations. This report provides an overview on the general approach of clinical implementation and testing of MC-based TPS with a specific focus on models of clinical photon and electron beams. Different types of beam models are described including those that utilize MC simulation of the treatment head and those that rely on analytical methods and measurements. The trade-off between accuracy and efficiency in the various source-modeling approaches is discussed together with guidelines for acceptance testing of MC-based TPS from the clinical standpoint. Specific recommendations are given on methods and practical procedures to commission clinical beam models for MC-based TPS.

A standardized commissioning framework of Monte Carlo dose calculation algorithms for proton pencil beam scanning treatment planning systems.

PURPOSE: Treatment planning systems (TPSs) from different vendors can involve different implementations of Monte Carlo dose calculation (MCDC) algorithms for pencil beam scanning (PBS) proton therapy. There are currently no guidelines for validating non-water materials in TPSs. Furthermore, PBS-specific parameters can vary by 1-2 orders of magnitude among different treatment delivery systems (TDSs). This paper proposes a standardized framework on the use of commissioning data and steps to validate TDS-specific parameters and TPS-specific heterogeneity modeling to potentially reduce these uncertainties. METHODS: A standardized commissioning framework was developed to commission the MCDC algorithms of RayStation 8A and Eclipse AcurosPT v13.7.20 using water and non-water materials. Measurements included Bragg peak depth-dose and lateral spot profiles and scanning field outputs for Varian ProBeam. The phase-space parameters were obtained from in-air measurements and the number of protons per MU from output measurements of 10 × 10 cm2 square fields at a 2 cm depth. Spot profiles and various PBS field measurements at additional depths were used to validate TPS. Human tissues in TPS, Gammex phantom materials, and artificial materials were used for the TPS benchmark and validation. RESULTS: The maximum differences of phase parameters, spot sigma, and divergence between MCDC algorithms are below 4.5 µm and 0.26 mrad in air, respectively. Comparing TPS to measurements at depths, both MC algorithms predict the spot sigma within 0.5 mm uncertainty intervals, the resolution of the measurement device. Beam Configuration in AcurosPT is found to underestimate number of protons per MU by ~2.5% and requires user adjustment to match measured data, while RayStation is within 1% of measurements using Auto model. A solid water phantom was used to validate the range accuracy of non-water materials within 1% in AcurosPT. CONCLUSIONS: The proposed standardized commissioning framework can detect potential issues during PBS TPS MCDC commissioning processes, and potentially can shorten commissioning time and improve dosimetric accuracies. Secondary MCDC can be used to identify the root sources of disagreement between primary MCDC and measurement.

Novel Monte Carlo dose calculation algorithm for robotic radiosurgery with multi leaf collimator: Dosimetric evaluation.

PURPOSE: At introduction in 2014, dose calculation for the first MLC on a robotic SRS/SBRT platform was limited to a correction-based Finite-Size Pencil Beam (FSPB) algorithm. We report on the dosimetric accuracy of a novel Monte Carlo (MC) dose calculation algorithm for this MLC, included in the Precision™ treatment planning system. METHODS: A phantom was built of one slab (5.0 cm) of lung-equivalent material (0.09…0.29 g/cc) enclosed by 3.5 cm (above) and 5 cm (below) slabs of solid water (1.045 g/cc). This was irradiated using rectangular (15.4 × 15.4 mm2 to 53.8 × 53.7 mm2) and two irregular MLC-fields. Radiochromic film (EBT3) was positioned perpendicular to the slabs and parallel to the beam. Calculated dose distributions were compared to film measurements using line scans and 2D gamma analysis. RESULTS: Measured and MC calculated percent depth dose curves showed a characteristic dose drop within the low-density region, which was not correctly reproduced by FSPB. Superior average gamma pass rates (2%/1 mm) were found for MC (91.2 ±â€¯1.5%) compared to FSPB (55.4 ±â€¯2.7%). However, MC calculations exhibited localized anomalies at mass density transitions around 0.15 g/cc, which were traced to a simplification in electron transport. Absence of these anomalies was confirmed in a modified build of the MC engine, which increased gamma pass rates to 96.6 ±â€¯1.2%. CONCLUSIONS: The novel MC algorithm greatly improves dosimetric accuracy in heterogeneous tissue, potentially expanding the clinical use of robotic radiosurgery with MLC. In-depth, independent validation is paramount to identify and reduce the residual uncertainties in any software solution.

Clinical evaluation for the difference of absorbed doses calculated to medium and calculated to water by Monte Carlo method.

BACKGROUND: To evaluate the difference of absorbed doses calculated to medium and to water by a Monte Carlo (MC) algorithm based treatment planning system (TPS), and to assess the potential clinical impact to dose prescription. METHODS: Thirty patients, 10 nasopharyngeal cancer (NPC), 10 lung cancer and 10 bone metastases cases, were selected for this study. For each case, the treatment plan was generated using a commercial MC based TPS and dose was calculated to medium (Dm). The plan was recalculated for dose to water (Dw) using the same Monitor Units (MU) and control points. The differences between Dm and Dw were qualitatively evaluated by dose-volume parameters and by the plan subtraction method. All plans were measured using the MapCheck2, and gamma passing rates were calculated. RESULTS: For NPC and Lung cases, the mean differences between Dw and Dm for the targets were less than 2% and the maximum difference was 3.9%. The maximum difference of D2% for the organs at risk (OARs) was 6.7%. The maximum differences between Dw and Dm were as high as 10% in certain high density regions. For bone metastases cases, the mean differences between Dw and Dm for the targets were more than 2.2% and the maximum difference was 7.1%. The differences between Dw and Dm for the OARs were basically negligible. At 3%&3 mm criterion, the gamma passing rate of Dw plan and Dm plan were close (> 94%). CONCLUSION: The differences between Dw and Dm has little clinical impact for most clinical cases. In bony structures the differences may become clinically significant if the target/OAR is receiving doses close to its tolerance limit which can potentially influence the selection or rejection of a particular plan.