Purpose: To compare a custom‐developed method for accurate dose recalculation of patient plans entered into clinical trials with results from a common treatment planning system. Method and Materials: A measurement‐driven multiple‐source model with the Dose Planning Method (DPM) Monte Carlo (MC) dose calculation algorithm was previously developed, validated, and benchmarked for the Varian 6 MV and 10 MV photon beams. Several patient cases have been recalculated and compared to the calculations from a Pinnacle planning system. Intensity modulated radiation therapy (IMRT) prostate, IMRT abdomen, stereotactic body radiotherapy lung, and IMRT lung patient cases were selected. Results: Field sizes from 4 cm × 4 cm to 40 cm × 40 cm were validated to within 2% of the maximum dose and 2 mm distance to agreement. At least 95% of the data tested met the validation criteria. Benchmark treatments planned using anthropomorphic phantoms were tested to within 3% of the target dose and 2 mm distance to agreement. At least 85% of the data tested met the benchmark criteria. Disagreement in the patient plan evaluation tended to occur at heterogeneity interfaces where electronic disequilibrium occurred, and in the beam penumbra, where scattered radiation was more prominent. The ratios of planning system calculation to MC calculation for the mean dose of the gross and planning target volumes for the patient plans ranged from 0.984 to 1.016. Conclusion: These results show that this MC software code generates answers similar to the Pinnacle system for IMRT and SBRT treatment plans. Differences are consistent with the superior physics modeling inherent in the Monte Carlo code. We believe the method will be useful for recalculating dose distributions for patients entered into clinical trials. Work supported by PHS CA010953, CA081647, and R01 CA85181 awarded by NCI, DHHS

Purpose: Validation and benchmarking of a newly‐developed measurement‐driven source model based on Monte Carlo calculations. Method and Materials: A measurement‐driven model using the Dose Planning Method DPM dose calculation algorithm is being developed for use with Varian, Elekta, and Siemens 6 MV and 10 MV photon beams. The present work details the validation and benchmarking for the Varian 6 MV beam. The multi‐source model consists of a primary photon point source, an extra‐focal exponential disk source, and an electron contamination uniform disk source. The model accounts for fluence and off‐axis energy effects due to the flattening filter. Dose calculations for field sizes from 4 cm by 4 cm to 40 cm by 40 cm were performed and tested against the basic beam data measurements. In addition, an IMRT homogeneous plan, a stereotactic lung plan, and an IMRT lung plan were delivered to anthropomorphic phantoms housing TLD and radiographic film dosimeters for benchmark evaluations. Results: Comparisons between calculation and measurement of the PDD and dose profiles for all square field size configurations showed agreement within 2%/2 mm for 90% of the data tested. General agreement at the level of 3%/2mm for 85% of the data tested was found in both of the lung treatment plans. However, calculation of the highly modulated IMRT homogeneous plan showed an underestimation of dose of up to 8% locally in the center of the PTV. Conclusion: This work demonstrates a source model that is robust for the Varian 6 MV photon beam; however, only a simple MLC model that did not include the effects of the rounded leaf ends and interleaf leakage was used. We are currently evaluating a detailed MLC model to improve the agreement with measurement.Conflict of Interest: Work supported by PHS CA010953, CA081647, and R01 CA85181 awarded by NCI, DHHS

Purpose/Objective(s)A key unmet need for adaptive radiotherapy treatment planning and improving radiotherapy workflow in general is a method that automatically generates high-quality IMRT plans. Automated treatment planning, if feasible, could also serve as a quality assurance ‘check’ compared to human treatment planning. We compared IMRT treatment plans, generated using hierarchical/prioritized optimization techniques (‘priopt’) and Monte Carlo dosimetry, against clinical Pinnacle treatment plans for definitive prostate treatments.Materials/MethodsSix randomly selected cases were replanned. The workflow consisted of: a fast, water-based dose calculation engine to generate the input beamlet influence matrix; a convex formulation of the treatment planning problem including ‘MOHx’ (mean-of-hottest x%) metrics as closely-correlated substitutes for dose-volume constraints; the MOSEK nonlinear solver; a leaf sequencing algorithm; a final Monte Carlo re-computation step (utilizing a commissioned head model that closely reproduces 6 MV Pinnacle IMRT results from our clinic); all embedded within the research system, CERR. Plans were compared with clinical Pinnacle treatment plans based on a list of planning metrics, and associated goals levels, elicited from the treating physician, including: Target max dose < 110% prescription dose (constraint); Target max dose < 107% prescription dose (preferred); Target D98 > prescription dose; Rectum V40 < 35%; Rectum V65 < 17%; Rectum V70 < 25%; Bladder V40 < 50%; Bladder V65 < 25%; and Bladder V70 < 25%.ResultsMost plan metrics met associated goals for most plans. Priopt plan metrics, averaged over the six cases, were better than Pinnacle for 8/9 metrics based on the simplified dosimetry, and better for 4/9 metrics after leaf-sequencing and final Monte Carlo recalculation. Except for the presence of greater dose heterogeneity allowed in the priopt plans, metric differences were small (usually on the order of a few percent) and seemed unlikely to lead to differing patient outcomes.ConclusionsRemarkably, priopt metrics were very similar to those achieved in the clinic. The results suggest that human planners relying on the Pinnacle system are working at close to optimal levels. The results further suggest that this algorithm could work well as an automated tool that produces high-quality dose distributions, with the caveat that more work needs to be done to slightly reduce the magnitude of hot regions. It therefore appears that this method of automated treatment planning is a good candidate to be further developed for several potential uses, including off-line adaptive re-planning, as a plan quality assurance check, or as a replacement for effort-intensive human planning. Purpose/Objective(s)A key unmet need for adaptive radiotherapy treatment planning and improving radiotherapy workflow in general is a method that automatically generates high-quality IMRT plans. Automated treatment planning, if feasible, could also serve as a quality assurance ‘check’ compared to human treatment planning. We compared IMRT treatment plans, generated using hierarchical/prioritized optimization techniques (‘priopt’) and Monte Carlo dosimetry, against clinical Pinnacle treatment plans for definitive prostate treatments. A key unmet need for adaptive radiotherapy treatment planning and improving radiotherapy workflow in general is a method that automatically generates high-quality IMRT plans. Automated treatment planning, if feasible, could also serve as a quality assurance ‘check’ compared to human treatment planning. We compared IMRT treatment plans, generated using hierarchical/prioritized optimization techniques (‘priopt’) and Monte Carlo dosimetry, against clinical Pinnacle treatment plans for definitive prostate treatments. Materials/MethodsSix randomly selected cases were replanned. The workflow consisted of: a fast, water-based dose calculation engine to generate the input beamlet influence matrix; a convex formulation of the treatment planning problem including ‘MOHx’ (mean-of-hottest x%) metrics as closely-correlated substitutes for dose-volume constraints; the MOSEK nonlinear solver; a leaf sequencing algorithm; a final Monte Carlo re-computation step (utilizing a commissioned head model that closely reproduces 6 MV Pinnacle IMRT results from our clinic); all embedded within the research system, CERR. Plans were compared with clinical Pinnacle treatment plans based on a list of planning metrics, and associated goals levels, elicited from the treating physician, including: Target max dose < 110% prescription dose (constraint); Target max dose < 107% prescription dose (preferred); Target D98 > prescription dose; Rectum V40 < 35%; Rectum V65 < 17%; Rectum V70 < 25%; Bladder V40 < 50%; Bladder V65 < 25%; and Bladder V70 < 25%. Six randomly selected cases were replanned. The workflow consisted of: a fast, water-based dose calculation engine to generate the input beamlet influence matrix; a convex formulation of the treatment planning problem including ‘MOHx’ (mean-of-hottest x%) metrics as closely-correlated substitutes for dose-volume constraints; the MOSEK nonlinear solver; a leaf sequencing algorithm; a final Monte Carlo re-computation step (utilizing a commissioned head model that closely reproduces 6 MV Pinnacle IMRT results from our clinic); all embedded within the research system, CERR. Plans were compared with clinical Pinnacle treatment plans based on a list of planning metrics, and associated goals levels, elicited from the treating physician, including: Target max dose < 110% prescription dose (constraint); Target max dose < 107% prescription dose (preferred); Target D98 > prescription dose; Rectum V40 < 35%; Rectum V65 < 17%; Rectum V70 < 25%; Bladder V40 < 50%; Bladder V65 < 25%; and Bladder V70 < 25%. ResultsMost plan metrics met associated goals for most plans. Priopt plan metrics, averaged over the six cases, were better than Pinnacle for 8/9 metrics based on the simplified dosimetry, and better for 4/9 metrics after leaf-sequencing and final Monte Carlo recalculation. Except for the presence of greater dose heterogeneity allowed in the priopt plans, metric differences were small (usually on the order of a few percent) and seemed unlikely to lead to differing patient outcomes. Most plan metrics met associated goals for most plans. Priopt plan metrics, averaged over the six cases, were better than Pinnacle for 8/9 metrics based on the simplified dosimetry, and better for 4/9 metrics after leaf-sequencing and final Monte Carlo recalculation. Except for the presence of greater dose heterogeneity allowed in the priopt plans, metric differences were small (usually on the order of a few percent) and seemed unlikely to lead to differing patient outcomes. ConclusionsRemarkably, priopt metrics were very similar to those achieved in the clinic. The results suggest that human planners relying on the Pinnacle system are working at close to optimal levels. The results further suggest that this algorithm could work well as an automated tool that produces high-quality dose distributions, with the caveat that more work needs to be done to slightly reduce the magnitude of hot regions. It therefore appears that this method of automated treatment planning is a good candidate to be further developed for several potential uses, including off-line adaptive re-planning, as a plan quality assurance check, or as a replacement for effort-intensive human planning. Remarkably, priopt metrics were very similar to those achieved in the clinic. The results suggest that human planners relying on the Pinnacle system are working at close to optimal levels. The results further suggest that this algorithm could work well as an automated tool that produces high-quality dose distributions, with the caveat that more work needs to be done to slightly reduce the magnitude of hot regions. It therefore appears that this method of automated treatment planning is a good candidate to be further developed for several potential uses, including off-line adaptive re-planning, as a plan quality assurance check, or as a replacement for effort-intensive human planning.

Purpose: We developed a Monte Carlo based IMRT recalculation tool and determined its parameters for Varian Clinac 2100C 6 MV and 18 MV photon beams. We report our comparisons with prostate and head and neck IMRT Pinnacle treatment plans. Method and Materials: Our source model components include: a primary photon point source, an extended extra‐focal source, and contamination electrons. One unique feature of the system is that it is fluence‐based, not a segment based calculation. A modified composite fluence map for each beam is built by summing the MLC segments and modifying for the effects of leakage, and rounded leaf edges. Model parameters are automatically determined by fitting to measurements. We re‐computed two 6 MV head & neck, and three 18 MV prostate, IMRT plans created by Pinnacle. For head & neck plans, we compared the DVHs of PTV, brainstem and parotid glands, as well as the mean dose to parotid glands. For the prostate plans, the DVHs for PTV, rectum, and bladder, as well as the D50, D98, and minimum doses for PTV are compared. Results: We found that our dose calculation system is comparable with Pinnacle for prostate IMRT plans, with small differences. For the prostate tests, the D50 for the PTV agrees within 0.7% with Pinnacle. DVHs for rectum and bladder all agree closely. The model predicts more pronounced dose inhomogeneity inside PTV in head and neck cases: the average reduction in the D98 value for the primary PTV was 5.5%. Conclusion: As expected, prostate IMRT recalculations agree well with the Pinnacle results. However, differences in head and neck results may be due to improved physics in the Monte Carlo system. The results support the use of the Monte Carlo tool as a treatment planning QA tool. Conflict of Interest: Work partially supported by grant PHS CA010953.

The Dose Planning Method (DPM) is one of several "fast" Monte Carlo (MC) computer codes designed to produce an accurate dose calculation for advanced clinical applications. We have developed a flexible machine modeling process and validation tests for open-field and IMRT calculations. To complement the DPM code, a practical and versatile source model has been developed, whose parameters are derived from a standard set of planning system commissioning measurements. The primary photon spectrum and the spectrum resulting from the flattening filter are modeled by a Fatigue function, cut-off by a multiplying Fermi function, which effectively regularizes the difficult energy spectrum determination process. Commonly-used functions are applied to represent the off-axis softening, increasing primary fluence with increasing angle ('the horn effect'), and electron contamination. The patient dependent aspect of the MC dose calculation utilizes the multi-leaf collimator (MLC) leaf sequence file exported from the treatment planning system DICOM output, coupled with the source model, to derive the particle transport. This model has been commissioned for Varian 2100C 6 MV and 18 MV photon beams using percent depth dose, dose profiles, and output factors. A 3-D conformal plan and an IMRT plan delivered to an anthropomorphic thorax phantom were used to benchmark the model. The calculated results were compared to Pinnacle v7.6c results and measurements made using radiochromic film and thermoluminescent detectors (TLD).

Purpose: To apply a measurement‐driven source model using the Monte Carlo Dose Planning Method (DPM) dose calculation engine to a Varian 10 MV photon beam. Method and Materials: A measurement‐driven model using the DPM dose calculation algorithm is being extended from a Varian 6 MV photon beam to include Varian 10 MV, Elekta 6 MV and 10 MV, and Siemens 6 MV and 10 MV photon beams. The present work details the model commissioning for the Varian 10 MV photon beam. The multi‐source model consists of a primary photon point source, an extra‐focal exponential disk source, and an electron contamination uniform disk source. The model accounts for fluence and off‐axis energy effects due to the flattening filter. The photon energy spectra for the primary and extra‐focal sources are modeled by the statistical fatigue‐failure function combined with a Fermi‐cutoff function. The energy spectrum of the electron contamination source is modeled as an exponential distribution. Model parameters are determined by an optimization process that minimizes the differences between measurement and calculation. The set of standard measurements used for optimizing consists of the percent depth dose (PDD) and dose profiles in water for 10×10 cm2 and 40×40 cm2 field sizes. Results: Comparisons between calculation and measurement of the PDD and dose profiles for the 10×10 cm2 field size show agreement within ±2%/2 mm except for the off‐axis low dose regions where calculations underestimate the dose by up to 3% of dmax. Conclusion: This work demonstrates that the model, previously shown to be accurate for the Varian 6 MV beam, can be successfully extended to the Varian 10 MV photon beam. Work is ongoing to further refine and validate the model to include Elekta and Siemens linear accelerators. Conflict of Interest: Work supported by PHS CA010953, CA081647, and R01 CA85181 awarded by NCI, DHHS.

Purpose: To benchmark a flexible Monte Carlo(MC) tool based on the Dose Planning Method (DPM) for use in evaluating Intensity Modulated Radiation Therapy(IMRT)treatment planning systems. Method and Materials: A dose calculation tool based on a flexible machine model using the Dose Planning Method (DPM),a “fast” Monte Carlo((MC)computer code, is being developed. Initial benchmark testing included a simple 10cm × 10cm multileaf collimator(MLC)diamond shaped pattern, a 3D conformal lung plan with the MLCs fully retracted, and an IMRTlung plan. Irradiations were performed using a 6MV photon beam from a Varian linear accelerator. Measurements were made in slab and anthropomorphic phantom geometries using thermoluminescent detectors(TLDs) and radiochromic film. The DPM calculation was then compared to measurements and also the calculation from the Pinnacle treatment planning system. Results: Profile comparisons from the MLCdiamond pattern irradiation showed good agreement in the penumbra region where MLC inter and intra leaf transmission effects were present. The point dose comparisons between the DPM calculation and measurement of the tumor for the 3D conformal and IMRTlung plans where within 2%. For the heart and spinal cord, the calculation for the 3D conformal and IMRTlung plans where within 7.5% of measurement, except in the conformal plan where the calculated dose point to the heart was positioned in a steep dose gradient and was 25% lower than measurement. Dose profiles through the center of the tumor showed good agreement in the PTV region, penumbra, and low doselung regions. Conclusion: This work demonstrates the feasibility of a source model based the DPM computer code to calculate dose distributions as part of the quality assurance program for clinical trials. Conflict of Interest: This work supported by PHS CA010953, CA081647, and CA085181 awarded by NCI, DHHS.

Purpose: Monte Carlo (MC) techniques are physically sound to provide accurate dose distributions. However, they take a large amount of CPU time compared to EGS4. Several fast MC algorithms have been developed, including VMC++ (Voxel Monte Carlo) and DPM (Dose Planning Method). For these fast MC codes, the simplifications of the underlying physics, variance reduction, and random number generation may not be equivalent. Moreover, implementation issues are complex and therefore testing and quality assurance is important. We compared these two codes as applied to heterogeneous media a quality assurance check. Methods and Materials: In this research, we conducted calculations for both codes on a standard open field water phantom, a water phantom with an air cavity, and a 5‐beam conformal therapy plan computed based on a CT‐scan of a heterogeneous anthropomorphic thorax phantom. The results were either compared with BEAM results, the Treatment Planning System (TPS; Pinnacle 7.6c), film or TLD measurements. The MC codes were integrated with CERR to facilitate CT‐based calculations. Results: In the water phantom, for 6MV 5×5cm2 field size at 100cm SSD, DPM and VMC++ agreed within 1%, except in the penumbra region. For 0.5×0.5cm2 field size of the air cavity test, they differed at the interface of air and water. For the 5‐beam 3D conformal plan on a thorax phantom, they agreed within 1% RMS ([STD of the difference larger than 5%Dmax]/Dmax); Most regions had a difference much less than 3% except at the buildup region for the two beams. Conclusions Carefully designed tests were conducted comparing DPM and VMC++. Water phantom results were almost identical. The air‐cavity‐heterogeneity results gave agreement within 1% except for the water‐air‐cavity interface. DPM appeared to be somewhat more sensitive to local material changes in the thorax phantom results.

Monte Carlo ( MC) dose calculations can be accurate but are also computationally intensive. In contrast, convolution superposition ( CS) offers faster and smoother results but bymaking approximations. We investigatedMC denoising techniques, which use available convolution superposition results and new noise filtering methods to guide and accelerateMC calculations. Two main approaches were developed to combine CS information with MC denoising. In the first approach, the denoising result is iteratively updated by adding the denoised residual difference between the result and theMC image. Multi- scale methods were used ( wavelets or contourlets) for denoising the residual. The iterations are initialized by the CS data. In the second approach, we used a frequency splitting technique by quadrature filtering to combine low frequency components derived from MC simulations with high frequency components derived from CS components. The rationale is to take the scattering tails as well as dose levels in the high- dose region from the MC calculations, which presumably more accurately incorporates scatter; high- frequency details are taken from CS calculations. 3D Butterworth filters were used to design the quadrature filters. The methods were demonstrated using anonymized clinical lung and head and neck cases. The MC dose distributions were calculated by the open- source dose planning method MC code with varying noise levels. Our results indicate that the frequency- splitting technique for incorporating CS- guided MC denoising is promising in terms of computational efficiency and noise reduction.

The purpose of this retrospective study was to assess how dosimetric parameters associated with radiation pneumonitis and local control in outcomes modeling change when dose calculations are based on more accurate four-dimensional computed tomography (4D-CT) data that characterizes patient specific breathing motion. 4-D CT scans were prospectively acquired via an IRB-approved protocol for lung cancer patients. Each 3D-CT volume in the 4D-CT dataset, as well as the clinical 3D-CT dataset, was registered to the 4D-CT end of exhalation (EoE) 3D-CT volume using an in-house developed deformable image registration software package integrated with our treatment planning research software system. The resulting deformation maps were used to deform the dose distributions computed using the DPM Monte Carlo method to the EoE phase geometry. Dose was thus calculated both for the clinical 3D-CT, as well as a respiratory-weighted 4D-CT composite dose distribution (referred to hereafter as the 'breathing-weighted'). The GTV mean dose, maximum dose, and D95 from the breathing-weighted distributions were smaller than those from the clinical dose distributions for all patients. Trends differed based on the disease site (upper lung versus lower lung). In contrast, lower lung patients had breathing-weighted minimum GTV doses that were on average 4.7% greater than their clinical dose counterparts (n = 2), while upper lung patients had breathing-weighted minimum GTV doses that were on average 7.4% lower than their clinical dose counterparts (n = 3). PTV mean, maximum, and D95 doses were lower for the breathing-weighted dose distributions vs. the clinical dose distributions for all patients. However, lower lung patients exhibited a mean decrease of 5.7% versus 2.0% for upper lung patients in the PTV maximum dose; and a decrease of approximately 5.0% versus 3.7% for upper lung patients in the PTV D95 dose. Lower lung patient mean lung doses (MLD), V10s, and V20s were always greater for the breathing-weighted dose distributions than for the clinical dose distributions (1.6%, 4.4%, and 2.0%, on average). In contrast to this, upper lung MLDs, V10s, and V20s were 3.4%, 1.8%, and 2.7% lower for the breathing-weighted dose distributions than for the clinical distributions (Fig.). Even for patients who have been planned with the advantage of 4D scans (such as done at our institution), breathing can change dosimetric parameters in ways which are expected to possibly impact outcome, especially for the target volumes. The spread in these values indicates that the effect of breathing motion is relatively patient specific.

Purpose: Monte Carlo (MC) may be advantageous as a basis for IMRT dose calculations as it provides for reliable heterogeneity corrections even in complex media. However, the upper‐limit of noise in pre‐computed beamlets has not been previously investigated. Method: We investigated convergence in plan metrics for optimized IMRT treatment plans as a function of MC noise in the input pre‐computed beamlet influence matrices. We analyzed seven individual patient cases of head and neck cancer. Each radiotherapy dose calculation involved nine 6 MV beams divided into beamlets of 1cm × 1cm cross‐section. The VMC++ Monte Carlo dose calculation engine was used as a basis for the calculations. A prioritized prescription optimization process was utilized to produce the final plan. As a function of beamlet noise levels, we monitored minimum combined‐PTV dose, D95 (minimum dose to hottest 95% of the combined PTVs), maximum dose to the brain stem, and mean dose to the parotid glands. For all metrics, we took a convergence within 1% of the low‐noise limit as being adequate. Results: For D95, 1% accuracy was typically achieved with an individual beamlet noise level of 2.4% (std. error); minimum target dose was accurate only when very noise levels were reached (< 0.5%); maximum dose to brain stem was accurate when beamlet noise was typically less than approximately 2%; mean dose to the parotid glands was accurate even at relatively noise beamlet levels (5%). Conclusions: Adequate MC beamlet smoothness depends critically on the plan review metrics. Metrics which partially or fully average over voxels (D95 of targets, or mean doses to organs) converge rather quickly compared to metrics which are sensitive to single‐voxel excursions (e.g., maximum organ dose or minimum target volume dose).This research was partially supported by NIH grant R01 CA90445 and a Siteman Cancer Center fellowship.

Purpose: We have developed a novel small animal radiation therapy device (microRT), which integrates multi‐modality imaging, radiation treatment planning, and conformal radiation therapy. In this study, we evaluated the accuracy of the treatment planning and positioning systems of the microRT device. Method and Materials: The microRT system utilizes a clinical 192Ir HDR source collimated via machined tungsten inserts to deliver photon beams at a source to target distances of 1–8cm at four angles (0, 90, 180, and 270). Beams were modeled using Monte Carlo and a parameterized analytic dose engine was created. Radiochromic film (5mm steps) in a solid water phantom was used to evaluate actual delivered doses in multiple planes. Treatment plans using these beams were created by a custom treatment planning system (microRTP) based on imported fiducial‐registered imaging (CT, MR, PET) of animals immobilized in the treatment position. A three‐axis computer‐controlled stage supports and positions animals in the beams according to the microRTP plan. Validation of the positioning system was performed using a phantom and images of phantom and collimator via a kV C‐arm. Results: The analytic dose model agreed with the Monte‐Carlo predicted dose within 5% and 10% outside and inside the 1 mm deep build‐up regions, respectively. Film dosimetry agreed with the analytic model within 10% and also demonstrated an effective field diameter of 8mm at 17mm from the source. The 192Ir line source geometry caused a radial anisotropy of up to 12% at 17 mm depth from the source. The positioning accuracy of the animal support hardware was sub‐millimeter. Conclusions: The microRT system provides conformal radiation therapy based on pre‐treatment imaging and planning for small animal models of cancer and tissue injury.This work supported in part by NIH R21 CA108677 and by a grant from Varian, Inc.

Purpose: Current IMRT QA methods are cumbersome and are not comprehensive. The purpose of the PlanCheck dose recalculation system is to provide independent verification that MLC leaf‐sequences generated by commercial treatment planning system will result in an acceptable dose. Method and Materials: The PlanCheck Beam Commissioning process was developed for medical linear accelerators and includes modeling of the photon energy spectra, off‐axis softening, electron contamination, flattening filter and penumbra blurring. The Monte Carlo beam parameters are derived by fitting treatment planning dose in water and to the measured dose. The system will regenerate the dose for each treatment and for the whole planned dose utilizing the Monte Carlo engine based on beam sequence DICOM/RTOG information imported into PlanCheck. The comparison metrics, including dose‐volume histogram comparisons, report the validation quality and dose agreement. Results: Monte Carlo commissioning was tested for Varian Linear Accelerator (Clinac 2100) for 2×2 cm2, 5×5 cm2, 10×10 cm2 and 20×20 cm2 open fields in water for 6MV, 10MV and 18MV photon beam. The profiles and comparison results show good agreement for Eclipse (Varian) open field dose in water. The IMRT treatment plans from systems such as XiO (CMS), Eclipse (Varian) and Pinnacle (Phillips) were tested with Plancheck for dose agreement with Monte Carlo Dose and found to show adequate agreement. Conclusion: PlanCheck Monte Carlo calculations shows good overall agreement with treatment planning results except for regions with complex heterogeneities. Sun Nuclear Corporation is currently developing this product for intensive commercial use. The system dose engine is currently in process of integration with a 64‐bit/16‐node calculation cluster, which we expect will make the typical IMRT plan calculation time 30 minutes or less for the total planned 3D dose.This research was partially supported by NIH grant R01 CA90445 and a grant from Sun Nuclear, Corp.

Medical PhysicsVolume 33, Issue 6Part17 p. 2199-2199 Therapy scientific session: Room 224 C TU-D-224C-05: Validation of a Linear Accelerator Source Model and Commissioning Process for Routine Clinical Monte Carlo Calculations J Cui, J Cui Sun Nuclear Corp., Melbourne, FL Washington University School of Medicine, Saint Louis, MOSearch for more papers by this authorK Zakaryan, K Zakaryan Sun Nuclear Corp., Melbourne, FL Washington University School of Medicine, Saint Louis, MOSearch for more papers by this authorJ Alaly, J Alaly Sun Nuclear Corp., Melbourne, FL Washington University School of Medicine, Saint Louis, MOSearch for more papers by this authorM Vicic, M Vicic Sun Nuclear Corp., Melbourne, FL Washington University School of Medicine, Saint Louis, MOSearch for more papers by this authorM Wiesmeyer, M Wiesmeyer Sun Nuclear Corp., Melbourne, FL Washington University School of Medicine, Saint Louis, MOSearch for more papers by this authorJ Deasy, J Deasy Sun Nuclear Corp., Melbourne, FL Washington University School of Medicine, Saint Louis, MOSearch for more papers by this author J Cui, J Cui Sun Nuclear Corp., Melbourne, FL Washington University School of Medicine, Saint Louis, MOSearch for more papers by this authorK Zakaryan, K Zakaryan Sun Nuclear Corp., Melbourne, FL Washington University School of Medicine, Saint Louis, MOSearch for more papers by this authorJ Alaly, J Alaly Sun Nuclear Corp., Melbourne, FL Washington University School of Medicine, Saint Louis, MOSearch for more papers by this authorM Vicic, M Vicic Sun Nuclear Corp., Melbourne, FL Washington University School of Medicine, Saint Louis, MOSearch for more papers by this authorM Wiesmeyer, M Wiesmeyer Sun Nuclear Corp., Melbourne, FL Washington University School of Medicine, Saint Louis, MOSearch for more papers by this authorJ Deasy, J Deasy Sun Nuclear Corp., Melbourne, FL Washington University School of Medicine, Saint Louis, MOSearch for more papers by this author First published: 11 July 2006 https://doi.org/10.1118/1.2241563Citations: 1About ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat Abstract Purpose: Many source models for Monte Carlo treatment planning include parameters which are difficult or ambiguous to determine. We developed and tested a straightforward source model and commissioning process for clinical Monte Carlo calculations. Method and Materials: Our commissioning process of fitting treatment planning system data includes fitting the photon spectrum, electron contamination, penumbra fluence blurring, jaw leakage, and the flattening filter effect. The energy spectrum is fit using a modified Fatigue Life Distribution multiplied by a Fermi. Electron contamination is modeled separately an exponential function, as suggested by Fippel. Penumbral blurring is modeled using a Gaussian filter. Leakage radiation is modeled as a low-intensity wide-field monoenergetic source. The flattening filter effect is modeled by multiplying the optimized fluence by a Gaussian reduction. The penumbra and the flattening filter are applied to the fluence map. We tested our methodology on doses produced by a Varian accelerator for 6 MV and 18 MV photons and 5×5, 10×10, and 20×20 cm2 field sizes. Results: We found that nine published photon spectra of Varian, Eleckta, and Siemens linear accelerators, ranging in energy from 4 MV to 25 MV could be modeled by the Fatigue Life Distribution with a Fermi cutoff. The agreement between the TPS doses and the commissioned MC doses were within 2%. Off-axis energy spectrum softening was unneeded. Conclusion: We have developed a straightforward, yet flexible source modeling system. The commissioning process affords a high-degree of automation with an unambiguous determination of the relevant parameters. Commissioning of clinical Monte Carlo treatment planning systems is facilitated by using a source model which is only as complicated as necessary to accurately simulates dose distributions. Conflict of Interest: This research was partially supported by NIH grant R01 CA90445 and a grant from Sun Nuclear, Corp. Citing Literature Volume33, Issue6Part17June 2006Pages 2199-2199 RelatedInformation

Purpose: To assess potential errors in radiographic film dosimetry in low density materials and to compare film measurements to dose estimates of a commercial convolution/superposition photon (CSP) dose calculation algorithm. Method and Materials: A standard film phantom was modified by replacing water‐equivalent slabs (30 HU) in its central portion with very low‐density material (−960 HU) to produce a lung slab phantom. Experiments were performed irradiating this phantom with 6 and 18 MV photons and field sizes of 2×2, 5×5, and 10×10 cm with 13 films placed between slabs. With unprocessed film in place, the phantom was then imaged in a computed tomography scanner and Monte Carlo (MC) and CSP calculations were done for each field size and energy combination. The phantom was then rescanned without film and dose was recalculated using MC to estimate the effect of the film in the prior MC calculations. Results: Measurements and MC calculations demonstrated field size and energy‐dependent dose perturbations at film planes in the low density material (up to 20% of maximum dose). In the phantom with film, central axis measurements and MC calculations matched within about 3%. The CSP algorithm was not perturbed by the film and overestimated dose in the low density region. Relying on film measurements alone would indicate a maximum overestimate of about 17% for 6 MV beams and 30% for 18 MV beams for the 2×2 cm fields. The filmless MC calculations show the true error to be about 6–9% higher. Conclusion: The error in CSP calculations will be underestimated if film is used as a dosimeter in very low‐density materials. The use of somewhat denser lung‐equivalent materials (e.g., −700 HU) would likely result in reduced, but still significant, error estimates. Supported by NIH grant RO1 CA85181 and a grant from Sun Nuclear, Corp.