Monte Carlo Dose Calculation Methods Research Articles

4089 Clinical Implementation of Monte Carlo Dose Calculation for Patient-Specific Radiotherapy Quality Assurance

OBJECTIVES/GOALS: The Monte Carlo dose calculation method is often considered the “gold standard” for patient dose calculations and can be as radiation dose measurements. Our study aims to develop a true Monte Carlo model that can be implemented in our clinic as part of our routine patient-specific quality assurance. METHODS/STUDY POPULATION: We have configured and validated a model of one of our linear accelerators used for radiation therapy treatments using the EGSnrc Monte Carlo simulation software. Measured dosimetric data was obtained from the linear accelerator and was used as the standard to compare the doses calculated with our model in EGSnrc. We will compare dose calculations between commercial treatment planning systems, the EGSnrc Monte Carlo model, and patient-specific measurements. We will implement the Monte Carlo model in our clinic for routine second-checks of patient plans, and to recalculate plans delivered to patients using machine log files. RESULTS/ANTICIPATED RESULTS: Our Monte Carlo model is within 1% agreement with our measured dosimetric data, and is an accurate representation of our linear accelerators used for patient treatments. With this high level of accuracy, we have begun simulating more complex patient treatment geometries, and expect the level of accuracy to be within 1% of measured data. We believe the Monte Carlo calculation based on machine log files will correlate with patient-specific QA analysis and results. The Monte Carlo model will be a useful tool in improving our patient-specific quality assurance protocol and can be utilized in further research. DISCUSSION/SIGNIFICANCE OF IMPACT: This work can be implemented directly in clinical practice to ensure patient doses are calculated as accurately as possible. These methods can be used by clinics who do not have access to more advanced dose calculation software, ensuring accuracy for all patients undergoing radiotherapy treatments.

4D Monte Carlo dose calculations for pre-treatment quality assurance of VMAT SBRT: a phantom-based feasibility study

Volumetric arc therapy (VMAT) for lung stereotactic body radiotherapy (SBRT) is challenging due to both breathing-induced motion and the dynamic components of the linear accelerator. In this study, a 4D Monte Carlo (4DMC) dose calculation method for VMAT SBRT is proposed and the feasibility of the method is evaluated. A rigidly-moving lung phantom was imaged using four dimensional computed tomography (4DCT). VMAT SBRT plans were generated on the average intensity projection dataset using the internal target volume (ITV) strategy (ITV-plan) and a single phase to simulate a dynamic treatment-couch tracking technique (TRACKING-plan). 4DMC simulations were performed and compared to 3D Monte Carlo (3DMC) and 3D- and 4D- calculations in the treatment planning system using the adaptive convolution (AC) algorithm. Dose metrics calculated for the ITV-plan showed an overestimation with 3D adaptive convolution (3DAC) for D (GTV) by 3.5% and by 2.0% for 3DMC, both compared to 4DMC. The TRACKING-plan D (GTV) calculated with the 3DAC method overestimated by 2.0% compared with 4DMC. Deviations between the calculation methods for Dmean (Lung) and D (PTV) were minimal. For both plans, measurements were taken with EBT3 film inside the phantom tumour. EBT3 film profiles showed good agreement with 4DMC for the TRACKING-plan giving a gamma pass rate of 97.2% for 3%/3 mm global and for 3DAC compared with measured, 95.8%. Whereas for the ITV-plan, the 3D profiles varied from film in the ITV periphery region with a pass rates of 50% and 48.6% for 3DAC and 3DMC, respectively. 4DMC agreed more closely to measurements for this plan with a pass rate of 95.8%. We have proposed an accurate method to perform 4D dose calculations for pre-treatment quality assurance of VMAT SBRT. The method was compared to experimental measurements and for both plans, 4DMC dose agreed with measurements more closely than other evaluated dose calculation methods. This study has demonstrated the feasibility of this 4DMC method.

SU‐F‐T‐56: Dosimetric Characterization of the INTRABEAM 50 KV X‐Ray System with a Needle Applicator in Heterogeneous Tissues

Purpose:We report the depth dose measurements in air, in solid water, and in bone materials for the Intrabeam 50 kV x‐rays with a needle applicator.Methods:The absolute dose was measured using a PTW TN34013W soft x‐ray ion chamber. Gammex tissue equivalent materials of solid water, inner bone, and cortical bone slabs (minimum thickness of 2 mm) were used. In addition, the PTW solid water slabs with a minimum thickness of 1 mm were used. The manufactory calibrated depth dose data in water were compared. The x‐ray source together with a needle applicator was secured on an Intrabeam stand. The slabs lay on a 6 degrees of freedom treatment couch with a digitally controlled minimum step size of 0.1 mm. The depth of the source to the ion chamber was accurately and reproducibly adjusted by moving the couch up and down.Results:The depth dose measurements for the Intrabeam 50 kV x‐rays with a needle applicator were conducted up to 20 mm in depth. The values for the PTW solid water were close to those for water. The Gammex solid water demonstrated lower values compared to water, consistent with the observation of its positive CT number. At a depth of 10 mm, the dose rates of the system are 29.6, 3.6, 1.2, and 0.24 Gy/min in air, in water, in inner bone, and in cortical bone, respectively. The 10 mm water equivalent depths in inner and cortical bone are about 6.4 and 4.1 mm. A function of power law combining exponential was used to fit and interpolate data well.Conclusion:Direct depth dose measurements in different materials provide a basis for treatment calculation and planning taking into account the heterogeneous effect. The results can be used for verification of analytical and/or Monte Carlo dose calculation methods as well.

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

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

An analytic linear accelerator source model for GPU-based Monte Carlo dose calculations

Recently, there has been a lot of research interest in developing fast Monte Carlo (MC) dose calculation methods on graphics processing unit (GPU) platforms. A good linear accelerator (linac) source model is critical for both accuracy and efficiency considerations. In principle, an analytical source model should be more preferred for GPU-based MC dose engines than a phase-space file-based model, in that data loading and CPU-GPU data transfer can be avoided. In this paper, we presented an analytical field-independent source model specifically developed for GPU-based MC dose calculations, associated with a GPU-friendly sampling scheme. A key concept called phase-space-ring (PSR) was proposed. Each PSR contained a group of particles that were of the same type, close in energy and reside in a narrow ring on the phase-space plane located just above the upper jaws. The model parameterized the probability densities of particle location, direction and energy for each primary photon PSR, scattered photon PSR and electron PSR. Models of one 2D Gaussian distribution or multiple Gaussian components were employed to represent the particle direction distributions of these PSRs. A method was developed to analyze a reference phase-space file and derive corresponding model parameters. To efficiently use our model in MC dose calculations on GPU, we proposed a GPU-friendly sampling strategy, which ensured that the particles sampled and transported simultaneously are of the same type and close in energy to alleviate GPU thread divergences. To test the accuracy of our model, dose distributions of a set of open fields in a water phantom were calculated using our source model and compared to those calculated using the reference phase-space files. For the high dose gradient regions, the average distance-to-agreement (DTA) was within 1 mm and the maximum DTA within 2 mm. For relatively low dose gradient regions, the root-mean-square (RMS) dose difference was within 1.1% and the maximum dose difference within 1.7%. The maximum relative difference of output factors was within 0.5%. Over 98.5% passing rate was achieved in 3D gamma-index tests with 2%/2 mm criteria in both an IMRT prostate patient case and a head-and-neck case. These results demonstrated the efficacy of our model in terms of accurately representing a reference phase-space file. We have also tested the efficiency gain of our source model over our previously developed phase-space-let file source model. The overall efficiency of dose calculation was found to be improved by ~1.3–2.2 times in water and patient cases using our analytical model.

TU-EF-304-07: Monte Carlo-Based Inverse Treatment Plan Optimization for Intensity Modulated Proton Therapy

Purpose: Intensity-modulated proton therapy (IMPT) is increasingly used in proton therapy. For IMPT optimization, Monte Carlo (MC) is desired for spots dose calculations because of its high accuracy, especially in cases with a high level of heterogeneity. It is also preferred in biological optimization problems due to the capability of computing quantities related to biological effects. However, MC simulation is typically too slow to be used for this purpose. Although GPU-based MC engines have become available, the achieved efficiency is still not ideal. The purpose of this work is to develop a new optimization scheme to include GPU-based MC into IMPT. Methods: A conventional approach using MC in IMPT simply calls the MC dose engine repeatedly for each spot dose calculations. However, this is not the optimal approach, because of the unnecessary computations on some spots that turned out to have very small weights after solving the optimization problem. GPU-memory writing conflict occurring at a small beam size also reduces computational efficiency. To solve these problems, we developed a new framework that iteratively performs MC dose calculations and plan optimizations. At each dose calculation step, the particles were sampled from different spots altogether with Metropolis algorithm, such that the particle number is proportional to the latest optimized spot intensity. Simultaneously transporting particles from multiple spots also mitigated the memory writing conflict problem. Results: We have validated the proposed MC-based optimization schemes in one prostate case. The total computation time of our method was ∼5–6 min on one NVIDIA GPU card, including both spot dose calculation and plan optimization, whereas a conventional method naively using the same GPU-based MC engine were ∼3 times slower. Conclusion: A fast GPU-based MC dose calculation method along with a novel optimization workflow is developed. The high efficiency makes it attractive for clinical usages.

Latent uncertainties of the precalculated track Monte Carlo method.

While significant progress has been made in speeding up Monte Carlo (MC) dose calculation methods, they remain too time-consuming for the purpose of inverse planning. To achieve clinically usable calculation speeds, a precalculated Monte Carlo (PMC) algorithm for proton and electron transport was developed to run on graphics processing units (GPUs). The algorithm utilizes pregenerated particle track data from conventional MC codes for different materials such as water, bone, and lung to produce dose distributions in voxelized phantoms. While PMC methods have been described in the past, an explicit quantification of the latent uncertainty arising from the limited number of unique tracks in the pregenerated track bank is missing from the paper. With a proper uncertainty analysis, an optimal number of tracks in the pregenerated track bank can be selected for a desired dose calculation uncertainty. Particle tracks were pregenerated for electrons and protons using EGSnrc and geant4 and saved in a database. The PMC algorithm for track selection, rotation, and transport was implemented on the Compute Unified Device Architecture (cuda) 4.0 programming framework. PMC dose distributions were calculated in a variety of media and compared to benchmark dose distributions simulated from the corresponding general-purpose MC codes in the same conditions. A latent uncertainty metric was defined and analysis was performed by varying the pregenerated track bank size and the number of simulated primary particle histories and comparing dose values to a "ground truth" benchmark dose distribution calculated to 0.04% average uncertainty in voxels with dose greater than 20% of Dmax. Efficiency metrics were calculated against benchmark MC codes on a single CPU core with no variance reduction. Dose distributions generated using PMC and benchmark MC codes were compared and found to be within 2% of each other in voxels with dose values greater than 20% of the maximum dose. In proton calculations, a small (≤ 1 mm) distance-to-agreement error was observed at the Bragg peak. Latent uncertainty was characterized for electrons and found to follow a Poisson distribution with the number of unique tracks per energy. A track bank of 12 energies and 60000 unique tracks per pregenerated energy in water had a size of 2.4 GB and achieved a latent uncertainty of approximately 1% at an optimal efficiency gain over DOSXYZnrc. Larger track banks produced a lower latent uncertainty at the cost of increased memory consumption. Using an NVIDIA GTX 590, efficiency analysis showed a 807 × efficiency increase over DOSXYZnrc for 16 MeV electrons in water and 508 × for 16 MeV electrons in bone. The PMC method can calculate dose distributions for electrons and protons to a statistical uncertainty of 1% with a large efficiency gain over conventional MC codes. Before performing clinical dose calculations, models to calculate dose contributions from uncharged particles must be implemented. Following the successful implementation of these models, the PMC method will be evaluated as a candidate for inverse planning of modulated electron radiation therapy and scanned proton beams.

SU-E-T-224: Is Monte Carlo Dose Calculation Method Necessary for Cyberknife Brain Treatment Planning?

Purpose: To study the dosimetric difference resulted in using the pencil beam algorithm instead of Monte Carlo (MC) methods for tumors adjacent to the skull. Methods: We retrospectively calculated the dosimetric differences between RT and MC algorithms for brain tumors treated with CyberKnife located adjacent to the skull for 18 patients (total of 27 tumors). The median tumor sizes was 0.53-cc (range 0.018-cc to 26.2-cc). The absolute mean distance from the tumor to the skull was 2.11 mm (range - 17.0 mm to 9.2 mm). The dosimetric variables examined include the mean, maximum, and minimum doses to the target, the target coverage (TC) and conformality index. The MC calculation used the same MUs as the RT dose calculation without further normalization and 1% statistical uncertainty. The differences were analyzed by tumor size and distance from the skull. Results: The TC was generally reduced with the MC calculation (24 out of 27 cases). The average difference in TC between RT and MC was 3.3% (range 0.0% to 23.5%). When the TC was deemed unacceptable, the plans were re-normalized in order to increase the TC to 99%. This resulted in a 6.9% maximum change in the prescription isodose line. The maximum changes in the mean, maximum, and minimum doses were 5.4 %, 7.7%, and 8.4%, respectively, before re-normalization. When the TC was analyzed with regards to target size, it was found that the worst coverage occurred with the smaller targets (0.018-cc). When the TC was analyzed with regards to the distance to the skull, there was no correlation between proximity to the skull and TC between the RT and MC plans. Conclusions: For smaller targets (< 4.0-cc), MC should be used to re-evaluate the dose coverage after RT is used for the initial dose calculation in order to ensure target coverage.

TH‐E‐BRE‐08: GPU‐Monte Carlo Based Fast IMRT Plan Optimization

Purpose:Intensity‐modulated radiation treatment (IMRT) plan optimization needs pre‐calculated beamlet dose distribution. Pencil‐beam or superposition/convolution type algorithms are typically used because of high computation speed. However, inaccurate beamlet dose distributions, particularly in cases with high levels of inhomogeneity, may mislead optimization, hindering the resulting plan quality. It is desire to use Monte Carlo (MC) methods for beamlet dose calculations. Yet, the long computational time from repeated dose calculations for a number of beamlets prevents this application. It is our objective to integrate a GPU‐based MC dose engine in lung IMRT optimization using a novel two‐steps workflow.Methods:A GPU‐based MC code gDPM is used. Each particle is tagged with an index of a beamlet where the source particle is from. Deposit dose are stored separately for beamlets based on the index. Due to limited GPU memory size, a pyramid space is allocated for each beamlet, and dose outside the space is neglected. A two‐steps optimization workflow is proposed for fast MC‐based optimization. At first step, rough beamlet dose calculations is conducted with only a small number of particles per beamlet. Plan optimization is followed to get an approximated fluence map. In the second step, more accurate beamlet doses are calculated, where sampled number of particles for a beamlet is proportional to the intensity determined previously. A second‐round optimization is conducted, yielding the final Result.Results:For a lung case with 5317 beamlets, 105 particles per beamlet in the first round, and 108 particles per beam in the second round are enough to get a good plan quality. The total simulation time is 96.4 sec.Conclusion:A fast GPU‐based MC dose calculation method along with a novel two‐step optimization workflow are developed. The high efficiency allows the use of MC for IMRT optimizations.

GPU-Monte Carlo based fast IMRT plan optimization

Purpose: Intensity-modulated radiation treatment (IMRT) plan optimization needs pre-calculated beamlet dose distribution. Pencil-beam or superposition/convolution type algorithms are typically used because of high computation speed. However, inaccurate beamlet dose distributions, particularly in cases with high levels of inhomogeneity, may mislead optimization, hindering the resulting plan quality. It is desire to use Monte Carlo (MC) methods for beamlet dose calculations. Yet, the long computational time from repeated dose calculations for a number of beamlets prevents this application. It is our objective to integrate a GPU-based MC dose engine in lung IMRT optimization using a novel two-steps workflow. Methods : A GPU-based MC code gDPM is used. Each particle is tagged with an index of a beamlet where the source particle is from. Deposit dose are stored separately for beamlets based on the index. Due to limited GPU memory size, a pyramid space is allocated for each beamlet, and dose outside the space is neglected. A two-steps optimization workflow is proposed for fast MC-based optimization. At first step, a rough dose calculation is conducted with only a few number of particle per beamlet. Plan optimization is followed to get an approximated fluence map. In the second step, more accurate beamlet doses are calculated, where sampled number of particles for a beamlet is proportional to the intensity determined previously. A second-round optimization is conducted, yielding the final result. Results: For a lung case with 5317 beamlets, 105 particles per beamlet in the first round, and 108 particles per beam in the second round are enough to get a good plan quality. The total simulation time is 96.4 sec. Conclusion : A fast GPU-based MC dose calculation method along with a novel two-step optimization workflow are developed. The high efficiency allows the use of MC for IMRT optimizations. -------------------------------- Cite this article as: Li Y, Tian Z, Shi F, Jiang S, Jia X. GPU-Monte Carlo based fast IMRT plan optimization. Int J Cancer Ther Oncol 2014; 2 (2):020244. DOI: 10.14319/ijcto.0202.44

In vivo proton dosimetry using a MOSFET detector in an anthropomorphic phantom with tissue inhomogeneity

When in vivo proton dosimetry is performed with a metal‐oxide semiconductor field‐effect transistor (MOSFET) detector, the response of the detector depends strongly on the linear energy transfer. The present study reports a practical method to correct the MOSFET response for linear energy transfer dependence by using a simplified Monte Carlo dose calculation method (SMC). A depth‐output curve for a mono‐energetic proton beam in polyethylene was measured with the MOSFET detector. This curve was used to calculate MOSFET output distributions with the SMC (SMCMOSFET). The SMCMOSFET output value at an arbitrary point was compared with the value obtained by the conventional SMCPPIC, which calculates proton dose distributions by using the depth‐dose curve determined by a parallel‐plate ionization chamber (PPIC). The ratio of the two values was used to calculate the correction factor of the MOSFET response at an arbitrary point. The dose obtained by the MOSFET detector was determined from the product of the correction factor and the MOSFET raw dose. When in vivo proton dosimetry was performed with the MOSFET detector in an anthropomorphic phantom, the corrected MOSFET doses agreed with the SMCPPIC results within the measurement error. To our knowledge, this is the first report of successful in vivo proton dosimetry with a MOSFET detector.PACS number: 87.56.‐v

Determination of peripheral underdosage at the lung-tumor interface using Monte Carlo radiation transport calculations

Prediction of dose distributions in close proximity to interfaces is difficult. In the context of radiotherapy of lung tumors, this may affect the minimum dose received by lesions and is particularly important when prescribing dose to covering isodoses. The objective of this work is to quantify underdosage in key regions around a hypothetical target using Monte Carlo dose calculation methods, and to develop a factor for clinical estimation of such underdosage. A systematic set of calculations are undertaken using 2 Monte Carlo radiation transport codes (egsnrc and geant4). Discrepancies in dose are determined for a number of parameters, including beam energy, tumor size, field size, and distance from chest wall. Calculations were performed for 1-mm3 regions at proximal, distal, and lateral aspects of a spherical tumor, determined for a 6-MV and a 15-MV photon beam. The simulations indicate regions of tumor underdose at the tumor-lung interface. Results are presented as ratios of the dose at key peripheral regions to the dose at the center of the tumor, a point at which the treatment planning system (TPS) predicts the dose more reliably. Comparison with TPS data (pencil-beam convolution) indicates such underdosage would not have been predicted accurately in the clinic. We define a dose reduction factor (DRF) as the average of the dose in the periphery in the 6 cardinal directions divided by the central dose in the target, the mean of which is 0.97 and 0.95 for a 6-MV and 15-MV beam, respectively. The DRF can assist clinicians in the estimation of the magnitude of potential discrepancies between prescribed and delivered dose distributions as a function of tumor size and location. Calculation for a systematic set of “generic” tumors allows application to many classes of patient case, and is particularly useful for interpreting clinical trial data.

Influence of breast composition and interseed attenuation in dose calculations for post-implant assessment of permanent breast 103Pd seed implant

The impact of tissue heterogeneity and interseed attenuation is studied in post-implant evaluation of five clinical permanent breast 103Pd seed implants using the Monte Carlo (MC) dose calculation method. Dose metrics for the target (PTV) as well as an organ at risk (skin) are used to visualize the differences between a TG43-like MC method and more accurate MC methods capable of considering the breast tissue heterogeneity as well as the interseed attenuation. PTV dose is reduced when using a breast tissue model instead of water in MC calculations while the dose to the skin is increased. Furthermore, we investigate the effect of varying the glandular/adipose proportion of the breast tissue on dose distributions. The dose to the PTV (skin) decreases (increases) with the increasing adipose proportion inside the breast. In a complete geometry and compared to a TG43-like situation, the average PTV D90 reduction varies from 3.9% in a glandular breast to 35.5% when the breast consists entirely of adipose. The skin D10 increases by 28.2% in an entirely adipose breast. The results of this work show the importance of an accurate and patient-dependent breast tissue model to be used in the dosimetry for this kind of low energy implant.

SU-GG-T-452: In-Vivo Dosimetry Using a MOSFET Detector in an Anthropomorphic Phantom for Therapeutic Proton Beam

Propose: In‐vivo protondosimetry can be used to identify major deviations in a delivery of treatment. We challenged in‐vivo measurements with a MOSFETdetector for an anthropomorphic phantom with tissue inhomogeneities. Here, The MOSFET response depends strongly on the linear energy transfer (LET) for proton beam. Therefore, we developed a method to correct the MOSFET response for the LET dependence by using a simplified Monte Carlo dose calculation method (SMC). Method and Materials: A depth‐output curve in polyethylene for mono‐energetic proton beam was measured with the MOSFETdetector. To calculate MOSFET output distributions, the SMC (SMCMOSFET) used the depth‐output curve. Then, the MOSFET output value at an arbitrary point obtained by the SMCMOSFET were compared with a dose value obtained by a conventional SMC, which calculates proton dose distributions by using the depth‐dose curve measured with an ionization chamber. From the ratio of both values, we can calculate the correction factor of the MOSFET response at the arbitrary point. Finally, the dose obtained by the MOSFETdetector is given by the product of the correction factor and the MOSFET raw dose without the above correction. We performed in‐vivo protondosimetry with the MOSFETdetector in head and neck regions of an anthropomorphic phantom. Results: The results measured by the MOSFET without the correction at each measurement condition indicate a remarkable deviation from those of the SMC. On the other hand, the corrected MOSFET doses agreed with the results of the SMC within the measurement error. Conclusion: For dose measurements in the anthropomorphic phantom with tissue inhomogeneities, the corrected MOSFET dose agreed well with the SMC results within the measurement error. Using the correction method for the MOSFET response, we could succeed in‐vivo protondosimetry with the MOSFETdetectors for the first time.

Dosimetric consequences of misalignment and realignment in prostate 3DCRT using intramodality ultrasound image guidance

It is common practice to correct for interfraction motion by shifting the patient from reference skin marks to better align the internal target at the linear accelerator's isocenter. Shifting the patient away from skin mark alignment causes the radiation beams to pass through a patient geometry different from that planned. Yet, dose calculations on the new geometry are not commonly performed. The intention of this work was to compare the dosimetric consequences of treating the patient with and without setup correction for the common clinical scenario of prostate interfraction motion. In order to account for prostate motion, 32 patients initially aligned to the room lasers via skin marks were realigned under the treatment beams by shifting the treatment couch based on ultrasound image guidance. An intramodality 3D ultrasound image guidance system was used to determine the setup correction, so that errors stemming from different tissue representations on different imaging modalities were eliminated. Two scenarios were compared to the reference static treatment plan: (1) Uncorrected patient alignment and (2) corrected patient alignment. Prostate displacement statistics and the dose to the clinical target volume (CTV), bladder, and rectum are reported. Monte Carlo dose calculation methods were employed. Comparing the uncorrected and corrected scenarios using the static treatment plan as the reference, the average percent difference in D95 for the CTV improved from -5.1% (range -40%, 1.3%) to 0.0% (-3.5%, 2.0%) and the average percent difference in V90 for the bladder and rectum changed from -11% (-84%, 232%) to -8.3% (-61%, 5.2%) and from -47% (-100%, 108%) to 0.9% (-62%, 102%), respectively. There was no simple correlation between displacement and dose discrepancy before correction. After patient realignment, the prescribed dose to the CTV was achieved within 1% for 75% (24/32) of the patients. After patient realignment, 50% of the patients had doses that differed from the static treatment plan by 25% for the bladder and 8% for the rectum. The dose degradation due to prostate motion (before correction) is not accurately predicted from the average trends for all patients. Outliers included smaller displacements that lead to larger dosimetric differences in the corrected scenario, especially for the bladder and rectum, which exhibited doses substantially different from that planned.

SU‐FF‐T‐422: Dependency of Proton Dose On Tissue Composition Investigated Using Monte Carlo Models of Hounsfield Number Conversion and Cadaver‐Based Anatomical Data

Purpose: In this study, we performed dependency study of proton dose on tissue composition using Monte Carlo models of Hounsfield number conversion and cadaver‐based anatomical data Method and Materials: Monte Carlo methods provide the most accurate radiation dose calculation technology as it take into account detailed materials properties, such as materials composition, mass density and interaction cross section. Monte Carlo simulation calculates the energy deposit per mass of each small volume (voxel) after a patient is presented by a large number of voxels. Two methods of building patient‐specified Monte Carlo models have been used in this study: one is to convert patient's CT Hounsfield numbers to materials; the other way is assign anatomical detailed materials using cadavers' segments. Dose distribution and dose volume histogram were compared based on the Monte Carlo models. Results: The dose distribution at the iso‐center slice, the 95% did not cover conformally to the ROI for Hounsfield MC model with shifting 2∼3 mm superior to the ROI. Dose volume history for planning tumor volume (PTV), Brain, Pituitary and Chiasm were used for evaluating the effect of tissue composition. The mean doses difference for PTV was 2.1% for the cadaver‐based MC and Hounsfield conversion MC model. The mean dose difference for Brain, Pituitary and Chiasm was less than 1.0%. Conclusion: Proton radiation dose was calculated and closely compared using two Monte Carlo models: one from CT Hounsfield number conversion and the other from human anatomically detailed Cadaver segments. It is found that the effect of different tissue composition on proton radiation dose calculation is complex involving organs at risk. Our method using cadaver‐based Monte Carlo model for proton dose calculation was shown to be suitable for benchmarking other Monte Carlo dose calculation methods and for providing tissue heterogeneity correction due to the effect of tissue composition.

TH‐D‐ValA‐07: Clinical Use of Monte Carlo in Proton Therapy: Pencil‐Beam Algorithm Vs Monte Carlo for Proton Therapy of Skull‐Base and Para‐Spinal Tumors

Introduction: Pencil beam algorithms rely on kernels to model proton range in density‐scaled water equivalent material. Monte Carlo dose calculation methods are more accurate by design. This study addresses the issue of clinical significance of differences between a commercial pencil‐beam algorithm and Monte Carlo dose calculation.Skull‐base or para‐spinal tumors are challenging for dose calculations due to interfaces between high and low density areas in the irradiated volumes. In addition, for para‐spinal cases, there are often metallic implants that not only distort the CT image but also affect the accuracy of dose calculations. Methods: Proton treatment planning is done at our institution by using FOCUS/XiO (CMS Inc.). To do full Monte Carlo based dose calculation the treatment head was modeled including a simulation of the modulator wheel rotation as well as aperture and compensator. The patient CT data information was converted into materials with explicit element composition and density. All secondary particles were tracked.Treatment plans designed with the treatment planning program were recalculated with the Monte Carlo code and compared by analyzing dose distributions and dose volume histograms. Results: In general, we found good agreement. Small differences are in part due to the difference between dose to medium and dose to water. Significant differences were found in and near air cavities as well as in areas affected by high density implants. Conclusion: For some tumors in the head and neck region and near the spine, Monte Carlo based dose calculation shows significant differences compared to pencil‐beam algorithms. Consequently, at our institution, a sub‐set of the patient population receives Monte Carlo dose calculation to assist in treatment planning.

Verification of Water-Equivalent Model for Calculation of Multiple Scattering Effects in Simplified Monte Carlo Dose Calculation

A water-equivalent model of heterogeneous materials is employed in a simplified Monte Carlo dose calculation method. Using the model, calculation of multiple scattering effects, not in an actual material but in water, is conducted. In this paper, the effects calculated using the model were compared with results calculated in an actual material and their influence in terms of dose predictions due to the difference was verified. We found that it was appropriate for multiple scattering calculations in proton dose predictions to use the water-equivalent model.

Accelerating Monte Carlo simulations of radiation therapy dose distributions using wavelet threshold de-noising.

The Monte Carlo dose calculation method works by simulating individual energetic photons or electrons as they traverse a digital representation of the patient anatomy. However, Monte Carlo results fluctuate until a large number of particles are simulated. We propose wavelet threshold de-noising as a postprocessing step to accelerate convergence of Monte Carlo dose calculations. A sampled rough function (such as Monte Carlo noise) gives wavelet transform coefficients which are more nearly equal in amplitude than those of a sampled smooth function. Wavelet hard-threshold de-noising sets to zero those wavelet coefficients which fall below a threshold; the image is then reconstructed. We implemented the computationally efficient 9,7-biorthogonal filters in the C language. Transform results were averaged over transform origin selections to reduce artifacts. A method for selecting best threshold values is described. The algorithm requires about 336 floating point arithmetic operations per dose grid point. We applied wavelet threshold de-noising to two two-dimensional dose distributions: a dose distribution generated by 10 MeV electrons incident on a water phantom with a step-heterogeneity, and a slice from a lung heterogeneity phantom. Dose distributions were simulated using the Integrated Tiger Series Monte Carlo code. We studied threshold selection, resulting dose image smoothness, and resulting dose image accuracy as a function of the number of source particles. For both phantoms, with a suitable value of the threshold parameter, voxel-to-voxel noise was suppressed with little introduction of bias. The roughness of wavelet de-noised dose distributions (according to a Laplacian metric) was nearly independent of the number of source electrons, though the accuracy of the de-noised dose image improved with increasing numbers of source electrons. We conclude that wavelet shrinkage de-noising is a promising method for effectively accelerating Monte Carlo dose calculations by factors of 2 or more.

Intensity modulated proton therapy: a clinical example.

In this paper, we report on the clinical application of fully automated three-dimensional intensity modulated proton therapy, as applied to a 34-year-old patient presenting with a thoracic chordoma. Due to the anatomically challenging position of the lesion, a three-field technique was adopted in which fields incident through the lungs and heart, as well as beams directed directly at the spinal cord, could be avoided. A homogeneous target dose and sparing of the spinal cord was achieved through field patching and computer optimization of the 3D fluence of each field. Sensitivity of the resultant plan to delivery and calculational errors was determined through both the assessment of the potential effects of range and patient setup errors, and by the application of Monte Carlo dose calculation methods. Ionization chamber profile measurements and 2D dosimetry using a scintillator/CCD camera arrangement were performed to verify the calculated fields in water. Modeling of a 10% overshoot of proton range showed that the maximum dose to the spinal cord remained unchanged, but setup error analysis showed that dose homogeneity in the target volume could be sensitive to offsets in the AP direction. No significant difference between the MC and analytic dose calculations was found and the measured dosimetry for all fields was accurate to 3% for all measured points. Over the course of the treatment, a setup accuracy of +/-4 mm (2 s.d.) could be achieved, with a mean offset in the AP direction of 0.1 mm. Inhalation/exhalation CT scans indicated that organ motion in the region of the target volume was negligible. We conclude that 3D IMPT plans can be applied clinically and safely without modification to our existing delivery system. However, analysis of the calculated intensity matrices should be performed to assess the practicality, or otherwise, of the plan.