A multi-modal diffusion model for noise reduction of particle number limited Monte Carlo dose calculation for carbon ion radiotherapy.

In the previous treatment planning system (TPS) for CyberKnife (CK), multileaf collimator (MLC)-based treatment plans could be created only by using the finite-size pencil beam (FSPB) algorithm. Recently, a new TPS, includingthe FSPB with lateral scaling option (FSPB+) and Monte Carlo (MC) algorithms, was developed. In this study, we performed basic and clinical end-to-end evaluations for MLC-based CK tumor-tracking radiotherapy using the MC, FSPB+, and FSPB. Water- and lung-equivalent slab phantoms were combined to obtain the percentage depth dose (PDD) and off-center ratio (OCR). The CK M6 system and Precision TPS were employed, and PDDs and OCRs calculated by the MC, FSPB+, and FSPB were compared with the measured doses obtained for 30.8×30.8mm2 and 60.0×61.6mm2 fields. A lung motion phantom was used for clinical evaluation and MLC-based treatment plans were created using the MC. The doses were subsequently recalculated using the FSPB+ and FSPB, while maintaining the irradiation parameters. The calculated doses were compared with the doses measured using a microchamber (for target doses) or a radiochromic film (for dose profiles). The dose volume histogram (DVH) indices were compared for all plans. In homogeneous and inhomogeneous phantom geometries, thePDDs calculated bythe MC and FSPB+agreed with the measurements within ±2.0% for the region between the surface and a depth of 250mm, whereasthe doses calculated by the FSPB in the lung-equivalent phantom region were noticeably higher than the measurements, and the maximum dose differenceswere 6.1% and 4.4% for the 30.8×30.8mm2 and 60.0×61.6mm2 fields, respectively. The maximum distance to agreement values of the MC, FSPB+, and FSPB at the penumbra regions of OCRswere 1.0, 0.6, and 1.1mm, respectively, but the best agreement was obtained between the MC-calculated curve and measurements at the boundary of the water- and lung-equivalent slabs, compared with those of the FSPB+ and FSPB.For clinical evaluations using the lung motion phantom, under the static motion condition, the dose errors measured by the microchamber were -1.0%, -1.9%, and 8.8% for MC, FSPB+, and FSPB, respectively; their gamma pass ratesfor the 3%/2mm criterion comparing to film measurement were 98.4%, 87.6%, and 31.4% respectively. Under respiratory motion conditions, there was no noticeable decline in the gamma pass rates. In the DVH indices, for most of the gross tumor volume and planning target volume, significant differences were observed between the MC and FSPB, and between the FSPB+ and FSPB. Furthermore, significant differences were observed for lung Dmean , V15 Gy , and V20 Gy between the MC, FSPB+, and FSPB. The results indicate that the doses calculated using the MC and FSPB+ differed remarkably in inhomogeneous regions, compared with the FSPB. Because the MC was the most consistent with the measurements, it is recommended for final dose calculations in inhomogeneous regions such as the lung. Furthermore, the sufficient accuracy of dose delivery using MLC-based tumor-tracking radiotherapy by CK was demonstrated for clinical implementation.

PurposeTo evaluate the quality of patient‐specific complicated treatment plans, including commercialized treatment planning systems (TPS) and commissioned beam data, we developed a process of quality assurance (QA) using a Monte Carlo (MC) platform. Specifically, we constructed an interface system that automatically converts treatment plan and dose matrix data in digital imaging and communications in medicine to an MC dose‐calculation engine. The clinical feasibility of the system was evaluated.Materials and MethodsA dose‐calculation engine based on GATE v8.1 was embedded in our QA system and in a parallel computing system to significantly reduce the computation time. The QA system automatically converts parameters in volumetric‐modulated arc therapy (VMAT) plans to files for dose calculation using GATE. The system then calculates dose maps. Energies of 6 MV, 10 MV, 6 MV flattening filter free (FFF), and 10 MV FFF from a TrueBeam with HD120 were modeled and commissioned. To evaluate the beam models, percentage depth dose (PDD) values, MC calculation profiles, and measured beam data were compared at various depths (Dmax, 5 cm, 10 cm, and 20 cm), field sizes, and energies. To evaluate the feasibility of the QA system for clinical use, doses measured for clinical VMAT plans using films were compared to dose maps calculated using our MC‐based QA system.ResultsA LINAC QA system was analyzed by PDD and profile according to the secondary collimator and multileaf collimator (MLC). Values for MC calculations and TPS beam data obtained using CC13 ion chamber (IBA Dosimetry, Germany) were consistent within 1.0%. Clinical validation using a gamma index was performed for VMAT treatment plans using a solid water phantom and arbitrary patient data. The gamma evaluation results (with criteria of 3%/3 mm) were 98.1%, 99.1%, 99.2%, and 97.1% for energies of 6 MV, 10 MV, 6 MV FFF, and 10 MV FFF, respectively.ConclusionsWe constructed an MC‐based QA system for evaluating patient treatment plans and evaluated its feasibility in clinical practice. We observed robust agreement between dose calculations from our QA system and measurements for VMAT plans. Our QA system could be useful in other clinical settings, such as small‐field SRS procedures or analyses of secondary cancer risk, for which dose calculations using TPS are difficult to verify.

Monte Carlo (MC) simulations are considered the gold-standard for accuracy in radiotherapy dose calculation; so far however, no commercial treatment planning system (TPS) provides a fast MC for supporting clinical practice in carbon ion therapy. To extend and validate the in-house developed fast MC dose engine MonteRay for carbon ion therapy, including physical and biological dose calculation. MonteRay is a CPU MC dose calculation engine written in C++ that is capable of simulating therapeutic proton, helium and carbon ion beams. In this work, development steps taken to include carbon ions in MonteRay are presented. Dose distributions computed with MonteRay are evaluated using a comprehensive validation dataset, including various measurements (pristine Bragg peaks, spread out Bragg peaks in water and behind an anthropomorphic phantom) and simulations of a patient plan. The latter includes both physical and biological dose comparisons. Runtimes of MonteRay were evaluated against those of FLUKA MC on a standard benchmark problem. Dosimetric comparisons between MonteRay and measurements demonstrated good agreement. In terms of pristine Bragg peaks, mean errors between simulated and measured integral depth dose distributions were between -2.3% and +2.7%. Comparing SOBPs at 5, 12.5 and 20cm depth, mean absolute relative dose differences were 0.9%, 0.7% and 1.6% respectively. Comparison against measurements behind an anthropomorphic head phantom revealed mean absolute dose differences of with global 3%/3mm 3D-γ passing rates of 99.3%, comparable to those previously reached with FLUKA (98.9%). Comparisons against dose predictions computed with the clinical treatment planning tool RayStation 11B for a meningioma patient plan revealed excellent local 1%/1mm 3D-γ passing rates of 98% for physical and 94% for biological dose. In terms of runtime, MonteRay achieved speedups against reference FLUKA simulations ranging from 14× to 72×, depending on the beam's energy and the step size chosen. Validations against clinical dosimetric measurements in homogeneous and heterogeneous scenarios and clinical TPS calculations have proven the validity of the physical models implemented in MonteRay. To conclude, MonteRay is viable as a fast secondary MC engine for supporting clinical practice in proton, helium and carbon ion radiotherapy.

Metal artefacts degrade clinical image quality which decreases the confidence of using computed tomography (CT) for the delineation of key structures for treatment planning and leads to dose errors in affected areas. In this work, we investigated accuracy of doses computed by the Eclipse treatment planning system near and inside metallic elements for two different computation algorithms. An impact of CT metal artefact reduction methods on the resulting calculated doses has also been assessed. A water phantom including Gafchromic film and metal inserts was irradiated (max dose 5 Gy) using a 6 MV photon beam. Three materials were tested: titanium, alloy 600, and tungsten. The phantom CT images were obtained with the pseudo-monoenergetic reconstruction (PMR) and the iterative metal artefact reduction (iMAR). Image sets were used for dose calculation using an Eclipse treatment planning station (TPS). Monte Carlo (MC) simulations were used to predict the true dose distribution in the phantom allowing for comparison with doses measured by film and calculated by TPS. Measured and simulated percentage depth doses (PDDs) were not statistically different (p > 0.618). Regional differences were observed at edges of metallic objects (max 8% difference). However, PDDs simulated with and without film were statistically different (p < 0.002). PDDs calculated by the Acuros XB algorithm based on the dose-to-medium approach best matched the MC reference regardless of the CT reconstruction methods and inserts used (p > 0.078). PDDs obtained using other algorithms significantly differ from the MC values (p < 0.011). The Acuros XB algorithm with a dose-to-medium approach provides reliable dose calculation in all metal regions when using the Varian system. The inability of the AAA algorithm to model backscatter dose significantly limits its clinical application in the presence of metal. No significant impact on the dose calculation was found for a range of metal artefact reduction strategies.

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.

Background:An increasing number of external beam treatment modalities including intensity modulated radiation therapy, volumetric modulated arc therapy (VMAT) and stereotactic radiosurgery uses very small fields for treatment planning and delivery. However, there are major challenges in small photon field dosimetry, due to the partial occlusion of the direct photon beam source’s view from the measurement point, lack of lateral charged particle equilibrium, steep dose-rate gradient and volume averaging effect of the detector response and variation of the energy fluence in the lateral direction of the beam. Therefore, experimental measurements of dosimetric parameters such as percent depth doses (PDDs), beam profiles and relative output factors (ROFs) for small fields continue to be a challenge.Materials and Methods:In this study, we used a homogeneous water phantom and the heterogeneous anthropomorphic stereotactic end-to-end verification (STEEV) head phantom for all dose measurements and calculations. PDDs, lateral dose profiles and ROFs were calculated in the Eclipse Treatment Planning System version 13·6 using the Acuros XB (AXB) and the analytical anisotropic algorithms (AAAs) in a homogenous water phantom. Monte Carlo (MC) simulations and measurements using the Exradin W1 Scintillator were also accomplished for four photon energies: 6 MV, 6FFF, 10 MV and 10FFF. Two VMAT treatment plans were generated for two different targets: one located in the brain and the other in the neck (close to the trachea) in the head phantom (CIRS, Norfolk, VA, USA). A Varian Truebeam linear accelerator (Varian, Palo Alto, CA, USA) was used for all treatment deliveries. Calculated results with AXB and AAA were compared with MC simulations and measurements.Results:The average difference of PDDs between W1 Exradin Scintillator measurements and MC simulations, AAA and AXB algorithm calculations were 1·2, 2·4 and 3·2%, respectively, for all field sizes and energies. AXB and AAA showed differences in ROF of about 0·3 and 2·9%, respectively, compared with W1 Exradin Scintillator measured values. For the target located in the brain in the head phantom, the average dose difference between W1 Exradin Scintillator and the MC simulations, AAA and AXB were 0·2, 3·2 and 2·7%, respectively, for all field sizes. Similarly, for the target located in the neck, the respective dose differences were 3·8, 5·7 and 3·5%.Conclusion:In this study, we compared dosimetric parameters such as PDD, beam profile and ROFs in water phantom and isocenter point dose measurements in an anthropomorphic head phantom representing a patient. We observed that measurements using the W1 Exradin scintillator agreed well with MC simulations and can be used efficiently for dosimetric parameters such as PDDs and dose profiles and patient-specific quality assurance measurements for small fields. In both homogenous and heterogeneous media, the AXB algorithm dose prediction agrees well with MC and measurements and was found to be superior to the AAA algorithm.

Quantitative assessment of the accuracy of dose calculation using pencil beam and Monte Carlo algorithms and requirements for clinical quality assurance

ObjectiveWe propose a deep learning-guided approach to generate voxel-based absorbed dose maps from whole-body CT acquisitions.MethodsThe voxel-wise dose maps corresponding to each source position/angle were calculated using Monte Carlo (MC) simulations considering patient- and scanner-specific characteristics (SP_MC). The dose distribution in a uniform cylinder was computed through MC calculations (SP_uniform). The density map and SP_uniform dose maps were fed into a residual deep neural network (DNN) to predict SP_MC through an image regression task. The whole-body dose maps reconstructed by the DNN and MC were compared in the 11 test cases scanned with two tube voltages through transfer learning with/without tube current modulation (TCM). The voxel-wise and organ-wise dose evaluations, such as mean error (ME, mGy), mean absolute error (MAE, mGy), relative error (RE, %), and relative absolute error (RAE, %), were performed.ResultsThe model performance for the 120 kVp and TCM test set in terms of ME, MAE, RE, and RAE voxel-wise parameters was − 0.0302 ± 0.0244 mGy, 0.0854 ± 0.0279 mGy, − 1.13 ± 1.41%, and 7.17 ± 0.44%, respectively. The organ-wise errors for 120 kVp and TCM scenario averaged over all segmented organs in terms of ME, MAE, RE, and RAE were − 0.144 ± 0.342 mGy, and 0.23 ± 0.28 mGy, − 1.11 ± 2.90%, 2.34 ± 2.03%, respectively.ConclusionOur proposed deep learning model is able to generate voxel-level dose maps from a whole-body CT scan with reasonable accuracy suitable for organ-level absorbed dose estimation.Clinical relevance statementWe proposed a novel method for voxel dose map calculation using deep neural networks. This work is clinically relevant since accurate dose calculation for patients can be carried out within acceptable computational time compared to lengthy Monte Carlo calculations.Key Points• We proposed a deep neural network approach as an alternative to Monte Carlo dose calculation.• Our proposed deep learning model is able to generate voxel-level dose maps from a whole-body CT scan with reasonable accuracy, suitable for organ-level dose estimation.• By generating a dose distribution from a single source position, our model can generate accurate and personalized dose maps for a wide range of acquisition parameters.

ID: 116 Comparison of beam output factors in MCNP6 and Geant4 based IAEA phase-space files

In proton therapy dose calculation, Monte Carlo (MC) simulations are superior in accuracy but more time consuming, compared to analytical calculations. Graphic processing units (GPUs) are effective in accelerating MC simulations but may suffer thread divergence and racing condition in GPU threads that degrades the computing performance due to the generation of secondary particles during nuclear reactions. A novel concept of virtual particle (VP) MC (VPMC) is proposed to avoid simulating secondary particles in GPU-accelerated proton MC dose calculation and take full advantage of the computing power of GPU. Neutrons and gamma rays were ignored as escaping from the human body; doses of electrons, heavy ions, and nuclear fragments were locally deposited; the tracks of deuterons were converted into tracks of protons. These particles, together with primary and secondary protons, are considered to be the realistic particles. Histories of primary and secondary protons were replaced by histories of multiple VPs. Each VP corresponded to one proton (either primary or secondary). A continuous-slowing-down-approximation model, an ionization model, and a large angle scattering event model corresponding to nuclear interactions were developed for VPs by generating probability distribution functions (PDFs) based on simulation results of realistic particles using MCsquare. For efficient calculations, these PDFs were stored in the Compute Unified Device Architecture textures. VPMC was benchmarked with TOPAS and MCsquare in phantoms and with MCsquare in 13 representative patient geometries. Comparisons between the VPMC calculated dose and dose measured in water during patient-specific quality assurance (PSQA) of the selected 13 patients were also carried out. Gamma analysis was used to compare the doses derived from different methods and calculation efficiencies were also compared. Integrated depth dose and lateral dose profiles in both homogeneous and inhomogeneous phantoms all matched well among VPMC, TOPAS, and MCsquare calculations. The 3D-3D gamma passing rates with a criterion of 2%/2mm and a threshold of 10% was 98.49% between MCsquare and TOPAS and 98.31% between VPMC and TOPAS in homogeneous phantoms, and 99.18% between MCsquare and TOPAS and 98.49% between VPMC and TOPAS in inhomogeneous phantoms, respectively. In patient geometries, the 3D-3D gamma passing rates with 2%/2mm/10% between dose distributions from VPMC and MCsquare were 98.56±1.09% in patient geometries. The 2D-3D gamma analysis with 3%/2mm/10% between the VPMC calculated dose distributions and the 2D measured planar dose distributions during PSQA was 98.91±0.88%. VPMC calculation was highly efficient and took 2.84±2.44s to finish for the selected 13 patients running on four NVIDIA Ampere GPUs in patient geometries. VPMC was found to achieve high accuracy and efficiency in proton therapy dose calculation.

Monte Carlo (MC) is widely recognized as the most accurate method for dosimetry analysis in radiotherapy due to its precision. However, successful MC dose calculation hinges upon the validation of the linac model employed in simulations. This study aims to verify the PRIMO model of the Varian Clinac iX and to determine the optimal initial electron energy. The comparison of one-dimensional dose distribution between simulations and measurements serves as the foundation for assessment. The Varian Clinac iX on 6 MV photon beam was meticulously modeled with the initial electron energies spanned from 5.2 to 5.8 MeV in increments of 0.2 MeV. The dose calculation were performed for a field size of 10 cm × 10 cm and a source-to-surface distance (SSD) of 100 cm. The Dose Planning Method (DPM) was adopted as the simulation engine for expedited MC simulation. A number of particle histories–approximately 4.0 × 108–were simulated, resulting in the generation of around 109 particles from the linac head. The investigation revealed that an initial electron energy of 5.8 MeV achieves good agreement with measurement by attaining the smallest difference in percentage depth dose (PDD) of about 0.98%. The lateral dose deviation of approximately 4.63% serves to validate the precision of the secondary collimator design. Additionally, a comparative analysis of DPM and PENELOPE for dose calculation was conducted. In contrast to the PENELOPE, the DPM speeds up simulation time by approximately 3.5 times, reduced statistical uncertainties to 0.59% and afford better accuracy in dose calculation. The result underscore the suitability of the PRIMO model for MC simulation for dose calculation, given its robust agreement with the measurements.

A Monte Carlo (MC) validation of the vendor‐supplied Varian TrueBeam 6 MV flattened (6X) phase‐space file and the first implementation of the Siebers‐Keall MC MLC model as applied to the HD120 MLC (for 6X flat and 6X flattening filterfree (6X FFF) beams) are described. The MC model is validated in the context of VMAT patient‐specific quality assurance. The Monte Carlo commissioning process involves: 1) validating the calculated open‐field percentage depth doses (PDDs), profiles, and output factors (OF), 2) adapting the Siebers‐Keall MLC model to match the new HD120‐MLC geometry and material composition, 3) determining the absolute dose conversion factor for the MC calculation, and 4) validating this entire linac/MLC in the context of dose calculation verification for clinical VMAT plans. MC PDDs for the 6X beams agree with the measured data to within 2.0% for field sizes ranging from 2 × 2 to 40 × 40 cm2. Measured and MC profiles show agreement in the 50% field width and the 80%‐20% penumbra region to within 1.3 mm for all square field sizes. MC OFs for the 2 to 40 cm2 square fields agree with measurement to within 1.6%. Verification of VMAT SABR lung, liver, and vertebra plans demonstrate that measured and MC ion chamber doses agree within 0.6% for the 6X beam and within 2.0% for the 6X FFF beam. A 3D gamma factor analysis demonstrates that for the 6X beam, > 99% of voxels meet the pass criteria (3%/3 mm). For the 6X FFF beam, > 94% of voxels meet this criteria. The TrueBeam accelerator delivering 6X and 6X FFF beams with the HD120 MLC can be modeled in Monte Carlo to provide an independent 3D dose calculation for clinical VMAT plans. This quality assurance tool has been used clinically to verify over 140 6X and 16 6X FFF TrueBeam treatment plans.PACS number: 87.55.K‐

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

A realistic X‐ray energy spectrum is essential for accurate dose calculation using the Monte Carlo (MC) algorithm. An energy spectrum for dose calculation in the radiation treatment planning system is modeled using the MC algorithm and adjusted to obtain acceptable agreement with the measured percent depth dose (PDD) and off‐axis ratio. The simulated energy spectrum may not consistently reproduce a realistic energy spectrum. Therefore, direct measurement of the X‐ray energy spectrum from a linac is necessary to obtain a realistic spectrum. Previous studies have measured low photon fluence directly, but the measurement was performed with a nonclinical linac with a thick target and a long target‐to‐detector distance. In this study, an X‐ray energy spectrum from a clinical linac was directly measured using a NaI(Tl) scintillator at an ultralow dose rate achieved by adjusting the gun grid voltage. The measured energy spectrum was unfolded by the Gold algorithm and compared with a simulated spectrum using statistical tests. Furthermore, the PDD was calculated using an unfolded energy spectrum and a simulated energy spectrum was compared with the measured PDD to evaluate the validity of the unfolded energy spectrum. Consequently, there was no significant difference between the unfolded and simulated energy spectra by nonparametric, Wilcoxon's rank‐sum, chi‐square, and two‐sample Kolmogorov–Smirnov tests with a significance level of 0.05. However, the PDD calculated from the unfolded energy spectrum better agreed with the measured compared to the calculated PDD results from the simulated energy spectrum. The adjustment of the incident electron parameters using MC simulation is sensitive and takes time. Therefore, it is desirable to obtain the energy spectrum by direct measurement. Thus, a method to obtain the realistic energy spectrum by direct measurement was proposed in this study.

To present our method and experience in commissioning dose models in water for spot scanning proton therapy in a commercial treatment planning system (TPS). The input data required by the TPS included in-air transverse profiles and integral depth doses (IDDs). All input data were obtained from Monte Carlo (MC) simulations that had been validated by measurements. MC-generated IDDs were converted to units of Gy mm(2)/MU using the measured IDDs at a depth of 2 cm employing the largest commercially available parallel-plate ionization chamber. The sensitive area of the chamber was insufficient to fully encompass the entire lateral dose deposited at depth by a pencil beam (spot). To correct for the detector size, correction factors as a function of proton energy were defined and determined using MC. The fluence of individual spots was initially modeled as a single Gaussian (SG) function and later as a double Gaussian (DG) function. The DG fluence model was introduced to account for the spot fluence due to contributions of large angle scattering from the devices within the scanning nozzle, especially from the spot profile monitor. To validate the DG fluence model, we compared calculations and measurements, including doses at the center of spread out Bragg peaks (SOBPs) as a function of nominal field size, range, and SOBP width, lateral dose profiles, and depth doses for different widths of SOBP. Dose models were validated extensively with patient treatment field-specific measurements. We demonstrated that the DG fluence model is necessary for predicting the field size dependence of dose distributions. With this model, the calculated doses at the center of SOBPs as a function of nominal field size, range, and SOBP width, lateral dose profiles and depth doses for rectangular target volumes agreed well with respective measured values. With the DG fluence model for our scanning proton beam line, we successfully treated more than 500 patients from March 2010 through June 2012 with acceptable agreement between TPS calculated and measured dose distributions. However, the current dose model still has limitations in predicting field size dependence of doses at some intermediate depths of proton beams with high energies. We have commissioned a DG fluence model for clinical use. It is demonstrated that the DG fluence model is significantly more accurate than the SG fluence model. However, some deficiencies in modeling the low-dose envelope in the current dose algorithm still exist. Further improvements to the current dose algorithm are needed. The method presented here should be useful for commissioning pencil beam dose algorithms in new versions of TPS in the future.