Monte Carlo Dose Calculation Research Articles

A Monte Carlo dose calculation system for ophthalmic brachytherapy based on a realistic eye model.

There is a growing trend towards the adoption of model-based calculation algorithms (MBDCAs) for brachytherapy dose calculations which can properly handle media and source/applicator heterogeneities. However, most of dose calculations in ocular plaque therapy are based on homogeneous water media and standard in-silico ocular phantoms, ignoring non-water equivalency of the anatomic tissues and heterogeneities in applicators and patient anatomy. In this work, we introduce EyeMC, a Monte Carlo (MC) model-based calculation algorithm for ophthalmic plaque brachytherapy using realistic and adaptable patient-specific eye geometries and materials. We used the MC code PENELOPE in EyeMC to model Bebig IsoSeed I25.S16 seeds in COMS plaques and 106 Ru/106 Rh applicators that are coupled onto a customizable eye model with realistic geometry and composition. To significantly reduce calculation times, we integrated EyeMC with CloudMC, a cloud computing platform for radiation therapy calculations. EyeMC is equipped with an evaluation module that allows the generation of isodose distributions, dose-volume histograms, and comparisons with Plaque Simulator three-dimensional dose distribution. We selected a sample of patients treated with 125 I and 106 Ru isotopes in our institution, covering a variety of different type of plaques, tumor sizes, and locations. Results from EyeMC were compared to the original plan calculated by the TPS Plaque Simulation, studying the influence of heterogeneous media composition as well. EyeMC calculations for Ru plaques agreed well with manufacturer's reference data and data of MC simulations from Hermida et al. (2013). Significant deviations, up to 20%, were only found in lateral profiles for notched plaques. As expected, media composition significantly affected estimated doses to different eye structures, especially in the 125 I cases evaluated. Dose to sclera and lens were found to be about 12% lower when considering real media, while average dose to tumor was 9% higher. 106 Ru cases presented a 1%-3% dose reduction in all structures using real media for calculation, except for the lens, which showed an average dose 7.6% lower than water-based calculations. Comparisons with Plaque Simulator calculations showed large differences in dose to critical structures for 106 Ru notched plaques. 125 I cases presented significant and systematic dose deviations when using the default calculation parameters from Plaque Simulator version 5.3.8., which were corrected when using calculation parameters from a custom physics model for carrier-attenuation and air-interface correction functions. EyeMC is a MC calculation system for ophthalmic brachytherapy based on a realistic and customizable eye-tumor model which includes the main eye structures with their real composition. Integrating this tool into a cloud computing environment allows to perform high-precision MC calculations of ocular plaque treatments in short times. The observed variability in eye anatomy among the selected cases justifies the use of patient-specific models.

Clinical verification of treatment planning dose calculation in lung SBRT with GATE Monte Carlo simulation code

Purpose This study aims to use GATE/Geant4 simulation code to evaluate the performance of dose calculations with Anisotropic Analytical Algorithm (AAA) in the context of lung SBRT for complex treatments considering images of patients. Methods Four cases of non-small cell lung cancer treated with SBRT were selected for this study. Irradiation plans were created with AAA and recalculated end to end using Monte Carlo (MC) method maintaining field configurations identical to the original plans. Each treatment plan was evaluated in terms of PTV and organs at risk (OARs) using dose-volume histograms (DVH). Dosimetric parameters obtained from DVHs were used to compare AAA and MC. Results The comparison between the AAA and MC DVH using gamma analysis with the passing criteria of 3%/3% showed an average passing rate of more than 90% for the PTV structure and 97% for the OARs. Tightening the criteria to 2%/2% showed a reduction in the average passing rate of the PTV to 86%. The agreement between the AAA and MC dose calculations for PTV dosimetric parameters (V100; V90; Homogeneity index; maximum, minimum and mean dose; CIPaddick and D2cm) was within 18.4%. For OARs, the biggest differences were observed in the spinal cord and the great vessels. Conclusions In general, we did not find significant differences between AAA and MC. The results indicate that AAA could be used in complex SBRT cases that involve a larger number of small treatment fields in the presence of tissue heterogeneities.

Evaluation of 4-Hz log files and secondary Monte Carlo dose calculation as patient-specific quality assurance for VMAT prostate plans.

PurposeIn this study, 4‐Hz log files were evaluated with an independent secondary Monte Carlo dose calculation algorithm to reduce the workload for patient‐specific quality assurance (QA) in clinical routine.Materials and MethodsA total of 30 randomly selected clinical prostate VMAT plans were included. The used treatment planning system (TPS) was Monaco (Elekta, Crawley), and the secondary dose calculation software was SciMoCa (Scientific‐RT, Munich). Monaco and SciMoCa work with a Monte Carlo algorithm. A plausibility check of Monaco and SciMoCa was performed using an ionization chamber in the BodyPhantom (BP). First, the original Monaco RT plans were verified with SciMoCa (pretreatment QA). Second, the corresponding 4‐Hz log files were converted into RT log file plans and sent to SciMoCa as on‐treatment QA. MLC shift errors were introduced for one prostate plan to determine the sensitivity of on‐treatment QA. For pretreatment and on‐treatment QA, a gamma analysis (2%/1mm/20%) was performed and dosimetric values of PTV and OARs were ascertained in SciMoCa.ResultsPlausibility check of TPS Monaco vs. BP measurement and SciMoCa vs. BP measurement showed valid accuracy for clinical VMAT QA. Using SciMoCa, there was no significant difference in PTV Dmean between RT plan and RT log file plan. Between pretreatment and on‐treatment QA, PTV metrics, femur right and left showed no significant dosimetric differences as opposed to OARs rectum and bladder. The overall gamma passing rate (GPR) ranged from 96.10% to 100% in pretreatment QA and from 93.50% to 99.80% in on‐treatment QA. MLC shift errors were identified for deviations larger than −0.50 mm and +0.75 mm using overall gamma criterion and PTV Dmean.ConclusionSciMoCa calculations of Monaco RT plans and RT log file plans are in excellent agreement to each other. Therefore, 4‐Hz log files and SciMoCa can replace labor‐intensive phantom‐based measurements as patient‐specific QA.

Development of clinical application program for radiotherapy induced cancer risk calculation using Monte Carlo engine in volumetric-modulated arc therapy

BackgroundThe purpose of this study is to develop a clinical application program that automatically calculates the effect for secondary cancer risk (SCR) of individual patient. The program was designed based on accurate dose calculations using patient computed tomography (CT) data and Monte Carlo engine. Automated patient-specific evaluation program was configured to calculate SCR.MethodsThe application program is designed to re-calculate the beam sequence of treatment plan using the Monte Carlo engine and patient CT data, so it is possible to accurately calculate and evaluate scatter and leakage radiation, difficult to calculate in TPS. The Monte Carlo dose calculation system was performed through stoichiometric calibration using patient CT data. The automatic SCR evaluation program in application program created with a MATLAB was set to analyze the results to calculate SCR. The SCR for organ of patient was calculated based on Biological Effects of Ionizing Radiation (BEIR) VII models. The program is designed to sequentially calculate organ equivalent dose (OED), excess absolute risk (EAR), excess relative risk (ERR), and the lifetime attributable risk (LAR) in consideration of 3D dose distribution analysis. In order to confirm the usefulness of the developed clinical application program, the result values from clinical application program were compared with the manual calculation method used in the previous study.ResultsThe OED values calculated in program were calculated to be at most approximately 13.3% higher than results in TPS. The SCR result calculated by the developed clinical application program showed a maximum difference of 1.24% compared to the result of the conventional manual calculation method. And it was confirmed that EAR, ERR and LAR values can be easily calculated by changing the biological parameters.ConclusionsWe have developed a patient-specific SCR evaluation program that can be used conveniently in the clinic. The program consists of a Monte Carlo dose calculation system for accurate calculation of scatter and leakage radiation and a patient-specific automatic SCR evaluation program using 3D dose distribution. The clinical application program that improved the disadvantages of the existing process can be used as an index for evaluating a patient treatment plan.

NRG Oncology Survey of Monte Carlo Dose Calculation Use in US Proton Therapy Centers.

Purpose/Objective(s)Monte Carlo (MC) dose calculation has appeared in primary commercial treatment-planning systems and various in-house platforms. Dual-energy computed tomography (DECT) and metal artifact reduction (MAR) techniques complement MC capabilities. However, no publications have yet reported how proton therapy centers implement these new technologies, and a national survey is required to determine the feasibility of including MC and companion techniques in cooperative group clinical trials.Materials/MethodsA 9-question survey was designed to query key clinical parameters: scope of MC utilization, validation methods for heterogeneities, clinical site-specific imaging guidance, proton range uncertainties, and how implants are handled. A national survey was distributed to all 29 operational US proton therapy centers on 13 May 2019.ResultsWe received responses from 25 centers (86% participation). Commercial MC was most commonly used for primary plan optimization (16 centers) or primary dose evaluation (18 centers), while in-house MC was used more frequently for secondary dose evaluation (7 centers). Based on the survey, MC was used infrequently for gastrointestinal, genitourinary, gynecology and extremity compared with other more heterogeneous disease sites (P < .007). Although many centers had published DECT research, only 3/25 centers had implemented DECT clinically, either in the treatment-planning system or to override implant materials. Most centers (64%) treated patients with metal implants on a case-by-case basis, with a variety of methods reported. Twenty-four centers (96%) used MAR images and overrode the surrounding tissue artifacts; however, there was no consensus on how to determine metal dimension, materials density, or stopping powers.ConclusionThe use of MC for primary dose calculation and optimization was prevalent and, therefore, likely feasible for clinical trials. There was consensus to use MAR and override tissues surrounding metals but no consensus about how to use DECT and MAR for human tissues and implants. Development and standardization of these advanced technologies are strongly encouraged for vendors and clinical physicists.

Impact of proton dose calculation algorithms on the interplay effect in PBS proton based SBRT lung plans

Purpose. The purpose of the current study was to investigate the impact of RayStation analytical pencil beam (APB) and Monte Carlo (MC) algorithms on the interplay effect in pencil beam scanning (PBS) proton-based stereotactic body radiation therapy (SBRT) lung plans. Methods. The current in-silico planning study was designed for a total dose of 5000 cGy(RBE) with a fractional dose of 1000 cGy(RBE). First, three sets of nominal plans were generated for each patient: (a) APB optimization followed by APB dose calculation (PB‐PB), (b) APB optimization followed by MC dose calculation (PB‐MC), and (c) MC optimization followed by MC dose calculation (MC‐MC). Second, for each patient, two sets of volumetric repainting plans (five repaintings) – PB-MCVR5 and MC-MCVR5 were generated based on PB-MC and MC-MC, respectively. Dosimetric differences between APB and MC algorithms were calculated on the nominal and interplay dose-volume-histograms (DVHs). Results. Interplay evaluation in non-volumetric repainting plans showed that APB algorithm overestimated the target coverage by up to 8.4% for D95% and 10.5% for D99%, whereas in volumetric repainting plans, APB algorithm overestimated by up to 5.3% for D95% and 7.0% for D99%. Interplay results for MC calculations showed a decrease in D95% and D99% by average differences of 3.5% and 4.7%, respectively, in MC‐MC plans and by 1.8% and 3.0% in MC-MCVR5 plans. Conclusion. In PBS proton-based SBRT lung plans, the combination of APB algorithm and interplay effect reduced the target coverage. This may result in inferior local control. The use of MC algorithm for both optimization and final dose calculations in conjunction with the volumetric repainting technique yielded superior target coverage.

A Pre-implementation Study of Volumetric Modulated Arc Therapy for Same-day Planning and Treatment of Vertebral Bone Metastases Within a Rapid-access Palliative Radiotherapy Programme

AimsWe aimed to develop a process for same-day contouring, planning, quality assurance and delivery of volumetric modulated arc therapy (VMAT) for vertebral bone metastases within our institution's rapid-access palliative radiotherapy programme. Materials and methodsTwo thoracic (T6–7, T3–7) and two lumbar (L2–3, L1–5) targets were contoured on computed tomography images acquired from an anthropomorphic phantom and five patient scans. Inverse planning aimed to provide coverage of a prescribed dose of 8 Gy with a combined lung V2Gy < 25% and a combined kidney mean dose <2 Gy. Serial plans were created to identify an efficient combination of six main planning variables specific to our treatment planning system: (i) voxel size (3 mm versus 5 mm), (ii) Monte Carlo statistical uncertainty (1% per calculation versus 3% per control point), (iii) fluence smoothing (medium versus high), (iv) number of iterations of segment shape changes during optimisation (1 versus 5), (v) dose calculation algorithm (Monte Carlo versus pencil beam) and (vi) number of arcs (single versus multiple). Contouring, planning, quality assurance and treatment delivery were timed. ResultsThe combination of planning variables deemed efficient and appropriate was: a 3 mm voxel size, statistical uncertainty of 1% per calculation, medium fluence smoothing, five iterations of segment shape changes, Monte Carlo dose calculation and single full arc delivery. Patient scan contouring times ranged from 7 to 9 min (T6–7), 11–13 min (T3–7), 5–7 min (L2–3) and 8–10 min (L1–5) and planning times ranged from 9 to 15 min (T6–7), 13–25 min (T3–7), 18–25 min (L2–3) and 21–31 min (L1–5). Physics quality assurance times ranged from 15 to 21 min and beam-on times ranged from 3 to 6 min. ConclusionsThe combined elements of VMAT for thoracic and lumbar vertebral bone metastases were completed in under 2 h. This new process makes same-day contouring, planning, quality assurance and treatment delivery of VMAT feasible within our rapid-access palliative radiotherapy programme.

Deep dose plugin: towards real-time Monte Carlo dose calculation through a deep learning-based denoising algorithm

Monte Carlo (MC) simulation is considered the gold standard method for radiotherapy dose calculation. However, achieving high precision requires a large number of simulation histories, which is time-consuming. The use of computer graphics processing units (GPUs) has greatly accelerated MC simulation and allows dose calculation within a few minutes for a typical radiotherapy treatment plan. However, some clinical applications demand real-time efficiency for MC dose calculation. To tackle this problem, we have developed a real-time, deep learning (DL)-based dose denoiser that can be plugged into a current GPU-based MC dose engine to enable real-time MC dose calculation. We used two different acceleration strategies to achieve this goal: (1) we applied voxel unshuffle and voxel shuffle operators to decrease the input and output sizes without any information loss, and (2) we decoupled the 3D volumetric convolution into a 2D axial convolution and a 1D slice convolution. In addition, we used a weakly supervised learning framework to train the network, which greatly reduces the size of the required training dataset and thus enables fast fine-tuning-based adaptation of the trained model to different radiation beams. Experimental results show that the proposed denoiser can run in as little as 39 ms, which is 11.6 times faster than the baseline model. As a result, the whole MC dose calculation pipeline can be finished within ∼ 0.15 s, including both GPU MC dose calculation and DL-based denoising, achieving the real-time efficiency needed for some radiotherapy applications, such as online adaptive radiotherapy.

Analysis of characteristics and validation of 6 MV photon beam produced by Elekta Synergy linear accelerator using EGSnrc Monte Carlo code

The most important step of the Monte Carlo (MC) dose calculation is the correct modelling of the radiation device. This study was aimed to determine the initial electron beam characteristics of the BEAMnrc model of 6 MV photon beam of Elekta Synergy IGRT System linear accelerator and to validate the simulation. For this purpose, EGSnrc/BEAMnrc/DOSXYZnrc Monte Carlo code were used for an accurate model of a 6 MV Elekta Synergy linac head. To analyze MC model, the dose distributions were calculated by EGSnrc/DOSXYZnrc simulation in a homogeneous water phantom. Then the results were compared with the measurements performed in the water phantom. In order to determine the mean energy and Full Width at Half Maximum (FWHM) suitable for the device in MC model, the Percentage of Depth Dose (PDD) and profile curves were compared for 10 × 10 cm2 field size. All data were analyzed using MATLAB. As a result, the mean energy of the device with the MC model was found to be compatible with the 6.2 MeV spectrum and 1 mm FWHM. For validation of MC model, PDD and profile curves for 5 × 5, 10 × 10, 15 × 15, 20 × 20 and 30 × 30 cm2 field sizes with 6.2 MeV spectrum and 1 mm FWHM were compared with water phantom measurement results.

Optimisation of variance reduction techniques in EGSnrc Monte Carlo for a 6 MV photon beam of an Elekta Synergy linear accelerator

ObjectiveMonte Carlo (MC) simulations are considered to be the most accurate form of algorithm for dose calculation. However, the main obstacle to using MC, especially in clinical routine, is the simulation time needed to gain results below a desirable level of uncertainty. Variance reduction techniques (VRTs) have been introduced to reduce the simulation time while maintaining the uncertainty at an acceptable level. The aim of this study is to investigate and optimize the VRTs implemented in EGSnrc MC code, BEAMnrc and DOSXYZnrc. MethodologyThe BEAMnrc user code was used to model a 10 × 10 cm2 field size of a 6 MV photon beam from an Elekta Synergy linear accelerator. The DOSXYZnrc user code was used to model a water phantom. The effects of different VRTs on the simulation efficiency were investigated either individually or in combination. The directional bremsstrahlung splitting (DBS) technique was investigated further to find the optimum splitting number and splitting field radius. For DOSXYZnrc, the photon splitting was investigated to find the best combination with the VRTs in BEAMnrc and to find the optimum splitting number. Finally, the best combination of VRTs in both BEAMnrc and DOSXYZnrc was compared with the corresponding phase space (PHSP) simulation source. ResultsThe DBS technique was found to be the most efficient. The optimum splitting number was found to be 10,000 and 15,000 with and without electron splitting, respectively. For the DBS splitting field radius, overestimating by up to 3 cm would be sufficient without causing a significant loss in efficiency. For both BEAMnrc and DOSXYZnrc, the combination of DBS, bremsstrahlung cross-section enhancement, range rejection with 2 MeV and photon splitting (with optimum splitting number of 35) was the most efficient, and was about 8% less efficient than PHSP simulation. ConclusionThe VRTs implemented in EGSnrc MC code made it possible to achieve an acceptably small uncertainty within a reasonable simulation time, if optimised properly. The combination of VRTs presented in this study eliminates the need to spare a large amount of disk space, and where parallel computing could allow for MC dose calculation in real-time adaptive treatment planning.

Validation of a secondary dose check tool against Monte Carlo and analytical clinical dose calculation algorithms in VMAT.

PurposePatient‐specific quality assurance (QA) is very important in radiotherapy, especially for patients with highly conformed treatment plans like VMAT plans. Traditional QA protocols for these plans are time‐consuming reducing considerably the time available for patient treatments. In this work, a new MC‐based secondary dose check software (SciMoCa) is evaluated and benchmarked against well‐established TPS (Monaco and Pinnacle3) by means of treatment plans and dose measurements.MethodsFifty VMAT plans have been computed using same calculation parameters with SciMoCa and the two primary TPSs. Plans were validated with measurements performed with a 3D diode detector (ArcCHECK) by translating patient plans to phantom geometry. Calculation accuracy was assessed by measuring point dose differences and gamma passing rates (GPR) from a 3D gamma analysis with 3%–2 mm criteria. Comparison between SciMoCa and primary TPS calculations was made using the same estimators and using both patient and phantom geometry plans.ResultsTPS and SciMoCa calculations were found to be in very good agreement with validation measurements with average point dose differences of 0.7 ± 1.7% and −0.2 ± 1.6% for SciMoCa and two TPSs, respectively. Comparison between SciMoCa calculations and the two primary TPS plans did not show any statistically significant difference with average point dose differences compatible with zero within error for both patient and phantom geometry plans and GPR (98.0 ± 3.0% and 99.0 ± 3.0% respectively) well in excess of the typical 95%clinical tolerance threshold.ConclusionThis work presents results obtained with a significantly larger sample than other similar analyses and, to the authors' knowledge, compares SciMoCa with a MC‐based TPS for the first time. Results show that a MC‐based secondary patient‐specific QA is a clinically viable, reliable, and promising technique, that potentially allows significant time saving that can be used for patient treatment and a per‐plan basis QA that effectively complements traditional commissioning and calibration protocols.

Cardiac-Sparing and Breast-Sparing Whole Lung Irradiation Using Intensity-Modulated Proton Therapy.

PurposeWhole lung irradiation (WLI) is indicated for certain pediatric patients with lung metastases. This study investigated whether WLI delivered as intensity-modulated proton therapy (IMPT) could significantly spare the heart and breasts when compared with conventional WLI delivered with anteroposterior/posteroanterior photon fields and with intensity-modulated photon therapy (IMRT) WLI.Materials and MethodsConventional, IMRT, and IMPT plans were generated for 5 patients (aged 5-22 years). The prescription dose was 16.5 GyRBE in 1.5-GyRBE fractions. Conventional plans used 6-MV photons prescribed to the midline and a field-in-field technique to cover the planning target volume (the internal target volume [ITV] + 1 cm). IMRT plans used 6-MV photons with a 7-beam arrangement with dose prescribed to the planning target volume. IMPT plans used scenario-based optimization with 5% range uncertainty and 5-mm positional uncertainty to cover the ITV robustly. Monte Carlo dose calculation was used for all IMPT plans. Doses were compared with paired Student t test.ResultsThe ITV Dmean was similar for the IMPT, conventional, and IMRT plans, but the IMPT plans had a lower Dmin and a higher Dmax at tissue interfaces than conventional plans (Dmean ratio: 0.96, P > .05; Dmin ratio: 0.9, P < .001; Dmax ratio: 1.1, P = .014). Dmeans for breast and heart substructures were lower with IMPT plans than with conventional/IMRT plans (heart ratios, 0.63:0.73; left ventricle ratios, 0.61:0.72; right ventricle ratios, 0.45:0.57; left atrium ratios, 0.79:0.85; right atrium ratios, 0.81:0.86; left breast ratios, 0.40:0.51; right breast ratio, 0.46:0.52; all P < .05).ConclusionsIMPT resulted in comparable ITV coverage and lower mean doses to the heart and breasts when compared with other techniques. Whole lung irradiation delivered as IMPT warrants prospective evaluation in pediatric patients.

Improving proton dose calculation accuracy by using deep learning

Pencil beam (PB) dose calculation is fast but inaccurate due to the approximations when dealing with inhomogeneities. Monte Carlo (MC) dose calculation is the most accurate method but it is time consuming. The aim of this study was to develop a deep learning model that can boost the accuracy of PB dose calculation to the level of MC dose by converting PB dose to MC dose for different tumor sites. The proposed model uses the PB dose and computed tomography image as inputs to generate the MC dose. We used 290 patients (90 head and neck, 93 liver, 75 prostate and 32 lung) to train, validate, and test the model. For each tumor site, we performed four numerical experiments to explore various combinations of training datasets. Training the model on data from all tumor sites together and using the dose distribution of each individual beam as input yielded the best performance for all four tumor sites. The average gamma passing rate (1 mm/1%) between the converted and the MC dose was 92.8%, 92.7%, 89.7% and 99.6% for head and neck, liver, lung, and prostate test patients, respectively. The average dose conversion time for a single field was less than 4 s. The trained model can be adapted to new datasets through transfer learning. Our deep learning-based approach can quickly boost the accuracy of PB dose to that of MC dose. The developed model can be added to the clinical workflow of proton treatment planning to improve dose calculation accuracy.

THUBrachy: fast Monte Carlo dose calculation tool accelerated by heterogeneous hardware for high-dose-rate brachytherapy

The Monte Carlo (MC) simulation is regarded as the gold standard for dose calculation in brachytherapy, but it consumes a large amount of computing resources. The development of heterogeneous computing makes it possible to substantially accelerate calculations with hardware accelerators. Accordingly, this study develops a fast MC tool, called THUBrachy, which can be accelerated by several types of hardware accelerators. THUBrachy can simulate photons with energy less than 3 MeV and considers all photon interactions in the energy range. It was benchmarked against the American Association of Physicists in Medicine Task Group No. 43 Report using a water phantom and validated with Geant4 using a clinical case. A performance test was conducted using the clinical case, showing that a multicore central processing unit, Intel Xeon Phi, and graphics processing unit (GPU) can efficiently accelerate the simulation. GPU-accelerated THUBrachy is the fastest version, which is 200 times faster than the serial version and approximately 500 times faster than Geant4. The proposed tool shows great potential for fast and accurate dose calculations in clinical applications.

Comparison of weekly and daily online adaptation for head and neck intensity-modulated proton therapy

The high conformality of intensity-modulated proton therapy (IMPT) dose distributions causes treatment plans to be sensitive to geometrical changes during the course of a fractionated treatment. This can be addressed using adaptive proton therapy (APT). One important question in APT is the frequency of adaptations performed during a fractionated treatment, which is related to the question whether plan adaptation has to be done online or offline. The purpose of this work is to investigate the impact of weekly and daily online IMPT plan adaptation on the treatment quality for head and neck patients. A cohort of ten head and neck patients with daily acquired cone-beam CT (CBCT) images was evaluated retrospectively. Dose tracking of the IMPT treatment was performed for three scenarios: base plan with no adaptation (BP), weekly online adaptation (OAW), and daily online adaptation (OAD). Both adaptation schemes used an in-house developed online APT workflow, performing Monte Carlo dose calculations on scatter-corrected CBCTs. IMPT plan adaptation was achieved by only tuning the weights of a subset of beamlets, based on deformable image registration from the planning CT to each CBCT. Although OAD mitigated random delivery errors more effectively than OAW on a fraction per fraction basis, both OAW and OAD achieved the clinical goals for all ten patients, while BP failed for six cases. In the high-risk CTV, accumulated values of D 98% ranged between 97.15% and 99.73% of the prescription dose for OAD, with a median of 98.07%. For OAW, values between 95.02% and 99.26% were obtained, with a median of 97.61% of the prescription dose. Otherwise, the dose to most organs at risk was similar for all three scenarios. Globally, our results suggest that OAW could be used as an alternative approach to OAD for most patients in order to reduce the clinical workload.

Dosimetric investigation of 103Pd permanent breast seed implant brachytherapy based on Monte Carlo calculations

PurposePermanent breast seed implant using 103Pd is emerging as an effective adjuvant radiation technique for early stage breast cancer. However, clinical dose evaluations follow the water-based TG-43 approach with its considerable approximations. Toward clinical adoption of advanced TG-186 model-based dose evaluations, this study presents a comprehensive investigation for permanent breast seed implant considering both target and normal tissue doses. Methods and MaterialsDose calculations are performed with the free open-source Monte Carlo (MC) code, egs_brachy, using two types of virtual patient models: TG43sim (simulated TG-43 conditions) and MCref (heterogeneous tissue modeling from patient CT, seeds at implant angle) for 35 patients. The sensitivity of dose metrics to seed orientation and tissue segmentation are assessed. ResultsIn the target volume, D90 is 14.1 ± 5.8% lower with MCref than with TG43sim, on average. Conversely, normal tissue doses are generally higher with MCref than with TG43sim, for example, by 22 ± 13% for skin D1cm2, 82 ± 7% for ribs Dmax, and 71 ± 23% for heart D1cm3. Discrepancies between MCref and TG43sim doses vary over the patient cohort, as well as with the tissue and metric considered. Skin doses are particularly sensitive to seed orientation, with average difference of 4% (maximum 28%) in D1cm2 for seeds modeled vertically (egs_brachy default) compared with those aligned with implant angle. ConclusionsTG-43 dose evaluations generally underestimate doses to critical normal organs/tissues while overestimating target doses. There is considerable variation in MCref and TG43sim on a patient-by-patient basis, motivating clinical adoption of patient-specific MC dose calculations. The MCref framework presented herein provides a consistent modeling approach for clinical implementation of advanced TG-186 dose calculations.

Dosimetric impact of random spot positioning errors in intensity modulated proton therapy plans of small and large volume tumors

To study dosimetric impact of random spot positioning errors on the clinical pencil beam scanning proton therapy plans. IMPT plans of 10 patients who underwent proton therapy for tumors in brain or pelvic regions representing small and large volumes, respectively, were included in the study. Spot positioning errors of 1 mm, -1 mm or ±1 mm were introduced in these clinical plans by modifying the geometrical co-ordinates of proton spots using a script in the MATLAB programming environment. Positioning errors were simulated to certain numbers of (20%, 40%, 60%, 80%) randomly chosen spots in each layer of these treatment plans. Treatment plans with simulated errors were then imported back to the Raystation (Version 7) treatment planning system and the resultant dose distribution was calculated using Monte-Carlo dose calculation algorithm.Dosimetric plan evaluation parameters for target and critical organs of nominal treatment plans delivered for clinical treatments were compared with that of positioning error simulated treatment plans. For targets, D95% and D2% were used for the analysis. Dose received by optic nerve, chiasm, brainstem, rectum, sigmoid, and bowel were analyzed using relevant plan evaluation parameters depending on the critical structure. In case of intracranial lesions, the dose received by 0.03 cm3 volume (D0.03 cm3) was analyzed for optic nerve, chiasm and brainstem. In rectum, the volume of it receiving a dose of 65 Gy(RBE) (V65) and 40 Gy(RBE) (V40) were compared between the nominal and error introduced plans. Similarly, V65 and V63 were analyzed for Sigmoid and V50 and V15 were analyzed for bowel. The maximum dose variation in PTV D95% (1.88 %) was observed in a brain plan in which the target volume was the smallest (2.7 cm3) among all 10 plans included in the study. This variation in D95% drops down to 0.3% for a sacral chordoma plan in which the PTV volume is significantly higher at 672 cm3. The maximum difference in OARs in terms of absolute dose (D0.03 cm3) was found in left optic nerve (9.81%) and the minimum difference was observed in brainstem (2.48%). Overall, the magnitude of dose errors in chordoma plans were less significant in comparison to brain plans. The dosimetric impact of different error scenarios in spot positioning becomes more prominent for treatment plans involving smaller target volume compared to plans involving larger target volumes. Provides information on the dosimetric impact of various possible spot positioning errors and its dependence on the tumor volume in intensity modulated proton therapy.

Comparison of the RayStation photon Monte Carlo dose calculation algorithm against measured data under homogeneous and heterogeneous irradiation geometries.

This work compares Monte Carlo dose calculations performed using the RayStation treatment planning system against data measured on a Varian Truebeam linear accelerator with 6 MV and 10 MV FFF photon beams. The dosimetric performance of the RayStation Monte Carlo calculations was evaluated in a variety of irradiation geometries employing homogeneous and heterogeneous phantoms. Profile and depth dose comparisons against measurement were carried out in relative mode using the gamma index as a quantitative measure of similarity within the central high dose regions. The results demonstrate that the treatment planning system dose calculation engine agrees with measurement to within 2%/1 mm for more than 95% of the data points in the high dose regions for all test cases. A systematic underestimation was observed at the tail of the profile penumbra and out of field, with mean differences generally<0.5mm or 1% of curve dose maximum respectively. Out of field agreement varied between evaluated beam models. The RayStation implementation of photon Monte Carlo dose calculations show good agreement with measured data for the range of scenarios considered in this work and is deemed sufficiently accurate for introduction into clinical use.

A novel proton counting detector and method for the validation of tissue and implant material maps for Monte Carlo dose calculation

The presence of artificial implants complicates the delivery of proton therapy due to inaccurate characterization of both the implant and the surrounding tissues. In this work, we describe a method to characterize implant and human tissue mimicking materials in terms of relative stopping power (RSP) using a novel proton counting detector. Each proton is tracked by directly measuring the deposited energy along the proton track using a fast, pixelated spectral detector AdvaPIX-TPX3 (TPX3). We considered three scenarios to characterize the RSPs. First, in-air measurements were made in the presence of metal rods (Al, Ti and CoCr) and bone. Then, measurements of energy perturbations in the presence of metal implants and bone in an anthropomorphic phantom were performed. Finally, sampling of cumulative stopping power (CSP) of the phantom were made at different locations of the anthropomorphic phantom. CSP and RSP information were extracted from energy spectra at each beam path. To quantify the RSP of metal rods we used the shift in the most probable energy (MPE) of CSP from the reference CSP without a rod. Overall, the RSPs were determined as 1.48, 2.06, 3.08, and 5.53 from in-air measurements; 1.44, 1.97, 2.98, and 5.44 from in-phantom measurements, for bone, Al, Ti and CoCr, respectively. Additionally, we sampled CSP for multiple paths of the anthropomorphic phantom ranging from 18.63 to 25.23 cm deriving RSP of soft tissues and bones in agreement within 1.6% of TOPAS simulations. Using minimum error of these multiple CSP, optimal mass densities were derived for soft tissue and bone and they are within 1% of vendor-provided nominal densities. The preliminary data obtained indicates the proposed novel method can be used for the validation of material and density maps, required by proton Monte Carlo Dose calculation, provided by competing multi-energy computed tomography and metal artifact reduction techniques.

Comparison of Air-Gaps Effect in a Small Cavity on Dose Calculation for 6 MV Linac.

Background: Many authors stated that cavities or air-gaps were the main challenge of dose calculation for head and neck with flattening filter medical linear accelerator (Linac) irradiation.Objective: The study aimed to evaluate the effect of air-gap dose calculation on flattening-filter-free (FFF) small field irradiation.Material and Methods: In this comparative study, we did the experimental and Monte Carlo (MC) simulation to evaluate the presence of heterogeneities in radiotherapy. We simulated the dose distribution on virtual phantom and the patient’s CT image to determine the air-gap effect of open small field and modulated photon beam, respectively. The dose ratio of air-gaps to tissue-equivalent was calculated both in Analytical Anisotropic Algorithm (AAA) and MC.Results: We found that the dose ratio of air to tissue-equivalent tends to decrease with a larger field size. This correlation was linear with a slope of -0.198±0.001 and -0.161±0.014 for both AAA and MC, respectively. On the other hand, the dose ratio below the air-gap was field size-dependent. The AAA to MC dose calculation as the impact of air-gap thickness and field size varied from 1.57% to 5.35% after the gap. Besides, patient’s skin and oral cavity on head and neck case received a large dose discrepancy according to this study. Conclusion: The dose air to tissue-equivalent ratio decreased with smaller air gaps and larger field sizes. Dose correction for AAA calculation of open small field size should be considered after small air-gaps. However, delivered beam from others gantry angle reduced this effect on clinical case.