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Title: TU-AB-BRC-11: Moving a GPU-OpenCL-Based Monte Carlo (MC) Dose Engine Towards Routine Clinical Use: Automatic Beam Commissioning and Efficient Source Sampling

Abstract

Purpose: We have previously developed a GPU-OpenCL-based MC dose engine named goMC with built-in analytical linac beam model. To move goMC towards routine clinical use, we have developed an automatic beam-commissioning method, and an efficient source sampling strategy to facilitate dose calculations for real treatment plans. Methods: Our commissioning method is to automatically adjust the relative weights among the sub-sources, through an optimization process minimizing the discrepancies between calculated dose and measurements. Six models built for Varian Truebeam linac photon beams (6MV, 10MV, 15MV, 18MV, 6MVFFF, 10MVFFF) were commissioned using measurement data acquired at our institution. To facilitate dose calculations for real treatment plans, we employed inverse sampling method to efficiently incorporate MLC leaf-sequencing into source sampling. Specifically, instead of sampling source particles control-point by control-point and rejecting the particles blocked by MLC, we assigned a control-point index to each sampled source particle, according to MLC leaf-open duration of each control-point at the pixel where the particle intersects the iso-center plane. Results: Our auto-commissioning method decreased distance-to-agreement (DTA) of depth dose at build-up regions by 36.2% averagely, making it within 1mm. Lateral profiles were better matched for all beams, with biggest improvement found at 15MV for which root-mean-square difference wasmore » reduced from 1.44% to 0.50%. Maximum differences of output factors were reduced to less than 0.7% for all beams, with largest decrease being from1.70% to 0.37% found at 10FFF. Our new sampling strategy was tested on a Head&Neck VMAT patient case. Achieving clinically acceptable accuracy, the new strategy could reduce the required history number by a factor of ∼2.8 given a statistical uncertainty level and hence achieve a similar speed-up factor. Conclusion: Our studies have demonstrated the feasibility and effectiveness of our auto-commissioning approach and new efficient source sampling strategy, implying the potential of our GPU-based MC dose engine goMC for routine clinical use.« less

Authors:
; ; ;  [1];  [2]
  1. UT Southwestern Medical Ctr, Dallas, TX (United States)
  2. Beihang University, Beijing (China)
Publication Date:
OSTI Identifier:
22653940
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 43; Journal Issue: 6; Other Information: (c) 2016 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
61 RADIATION PROTECTION AND DOSIMETRY; COMMISSIONING; DEPTH DOSE DISTRIBUTIONS; DIFFERENTIAL THERMAL ANALYSIS; LINEAR ACCELERATORS; MONTE CARLO METHOD; PHOTON BEAMS; SAMPLING

Citation Formats

Tian, Z, Folkerts, M, Jiang, S, Jia, X, and Li, Y. TU-AB-BRC-11: Moving a GPU-OpenCL-Based Monte Carlo (MC) Dose Engine Towards Routine Clinical Use: Automatic Beam Commissioning and Efficient Source Sampling. United States: N. p., 2016. Web. doi:10.1118/1.4957405.
Tian, Z, Folkerts, M, Jiang, S, Jia, X, & Li, Y. TU-AB-BRC-11: Moving a GPU-OpenCL-Based Monte Carlo (MC) Dose Engine Towards Routine Clinical Use: Automatic Beam Commissioning and Efficient Source Sampling. United States. doi:10.1118/1.4957405.
Tian, Z, Folkerts, M, Jiang, S, Jia, X, and Li, Y. Wed . "TU-AB-BRC-11: Moving a GPU-OpenCL-Based Monte Carlo (MC) Dose Engine Towards Routine Clinical Use: Automatic Beam Commissioning and Efficient Source Sampling". United States. doi:10.1118/1.4957405.
@article{osti_22653940,
title = {TU-AB-BRC-11: Moving a GPU-OpenCL-Based Monte Carlo (MC) Dose Engine Towards Routine Clinical Use: Automatic Beam Commissioning and Efficient Source Sampling},
author = {Tian, Z and Folkerts, M and Jiang, S and Jia, X and Li, Y},
abstractNote = {Purpose: We have previously developed a GPU-OpenCL-based MC dose engine named goMC with built-in analytical linac beam model. To move goMC towards routine clinical use, we have developed an automatic beam-commissioning method, and an efficient source sampling strategy to facilitate dose calculations for real treatment plans. Methods: Our commissioning method is to automatically adjust the relative weights among the sub-sources, through an optimization process minimizing the discrepancies between calculated dose and measurements. Six models built for Varian Truebeam linac photon beams (6MV, 10MV, 15MV, 18MV, 6MVFFF, 10MVFFF) were commissioned using measurement data acquired at our institution. To facilitate dose calculations for real treatment plans, we employed inverse sampling method to efficiently incorporate MLC leaf-sequencing into source sampling. Specifically, instead of sampling source particles control-point by control-point and rejecting the particles blocked by MLC, we assigned a control-point index to each sampled source particle, according to MLC leaf-open duration of each control-point at the pixel where the particle intersects the iso-center plane. Results: Our auto-commissioning method decreased distance-to-agreement (DTA) of depth dose at build-up regions by 36.2% averagely, making it within 1mm. Lateral profiles were better matched for all beams, with biggest improvement found at 15MV for which root-mean-square difference was reduced from 1.44% to 0.50%. Maximum differences of output factors were reduced to less than 0.7% for all beams, with largest decrease being from1.70% to 0.37% found at 10FFF. Our new sampling strategy was tested on a Head&Neck VMAT patient case. Achieving clinically acceptable accuracy, the new strategy could reduce the required history number by a factor of ∼2.8 given a statistical uncertainty level and hence achieve a similar speed-up factor. Conclusion: Our studies have demonstrated the feasibility and effectiveness of our auto-commissioning approach and new efficient source sampling strategy, implying the potential of our GPU-based MC dose engine goMC for routine clinical use.},
doi = {10.1118/1.4957405},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = {Wed Jun 15 00:00:00 EDT 2016},
month = {Wed Jun 15 00:00:00 EDT 2016}
}
  • Purpose: One of the most accurate methods for radiation transport is Monte Carlo (MC) simulation. Long computation time prevents its wide applications in clinic. We have recently developed a fast MC code for carbon ion therapy called GPU-based OpenCL Carbon Monte Carlo (goCMC) and its accuracy in physical dose has been established. Since radiobiology is an indispensible aspect of carbon ion therapy, this study evaluates accuracy of goCMC in biological dose and microdosimetry by benchmarking it with FLUKA. Methods: We performed simulations of a carbon pencil beam with 150, 300 and 450 MeV/u in a homogeneous water phantom using goCMCmore » and FLUKA. Dose and energy spectra for primary and secondary ions on the central beam axis were recorded. Repair-misrepair-fixation model was employed to calculate Relative Biological Effectiveness (RBE). Monte Carlo Damage Simulation (MCDS) tool was used to calculate microdosimetry parameters. Results: Physical dose differences on the central axis were <1.6% of the maximum value. Before the Bragg peak, differences in RBE and RBE-weighted dose were <2% and <1%. At the Bragg peak, the differences were 12.5% caused by small range discrepancy and sensitivity of RBE to beam spectra. Consequently, RBE-weighted dose difference was 11%. Beyond the peak, RBE differences were <20% and primarily caused by differences in the Helium-4 spectrum. However, the RBE-weighted dose agreed within 1% due to the low physical dose. Differences in microdosimetric quantities were small except at the Bragg peak. The simulation time per source particle with FLUKA was 0.08 sec, while goCMC was approximately 1000 times faster. Conclusion: Physical doses computed by FLUKA and goCMC were in good agreement. Although relatively large RBE differences were observed at and beyond the Bragg peak, the RBE-weighted dose differences were considered to be acceptable.« less
  • Purpose: (1) To perform phase space (PS) based source modeling for Tomotherapy and Varian TrueBeam 6 MV Linacs, (2) to examine the accuracy and performance of the ARCHER Monte Carlo code on a heterogeneous computing platform with Many Integrated Core coprocessors (MIC, aka Xeon Phi) and GPUs, and (3) to explore the software micro-optimization methods. Methods: The patient-specific source of Tomotherapy and Varian TrueBeam Linacs was modeled using the PS approach. For the helical Tomotherapy case, the PS data were calculated in our previous study (Su et al. 2014 41(7) Medical Physics). For the single-view Varian TrueBeam case, we analyticallymore » derived them from the raw patient-independent PS data in IAEA’s database, partial geometry information of the jaw and MLC as well as the fluence map. The phantom was generated from DICOM images. The Monte Carlo simulation was performed by ARCHER-MIC and GPU codes, which were benchmarked against a modified parallel DPM code. Software micro-optimization was systematically conducted, and was focused on SIMD vectorization of tight for-loops and data prefetch, with the ultimate goal of increasing 512-bit register utilization and reducing memory access latency. Results: Dose calculation was performed for two clinical cases, a Tomotherapy-based prostate cancer treatment and a TrueBeam-based left breast treatment. ARCHER was verified against the DPM code. The statistical uncertainty of the dose to the PTV was less than 1%. Using double-precision, the total wall time of the multithreaded CPU code on a X5650 CPU was 339 seconds for the Tomotherapy case and 131 seconds for the TrueBeam, while on 3 5110P MICs it was reduced to 79 and 59 seconds, respectively. The single-precision GPU code on a K40 GPU took 45 seconds for the Tomotherapy dose calculation. Conclusion: We have extended ARCHER, the MIC and GPU-based Monte Carlo dose engine to Tomotherapy and Truebeam dose calculations.« less
  • Purpose: To commission a new MLC model for the GEPTS Monte Carlo system. The model is based on the concept of leaves and interleaves effective densities Methods: GEPTS is a Monte Carlo system to be used for external beam planning verification. GEPTS incorporates detailed photon and electron transport algorithms (Med.Phys. 29, 2002, 835). A new GEPTS model for the Varian Millennium MLC is presented. The model accounts for: 1) thick (1 cm) and thin (0.5 cm) leaves, 2) tongue-and-groove design, 3) High-Transmission (HT) and Low-Transmission (LT) interleaves, and 4) rounded leaf end. Leaf (and interleaf) height is set equal tomore » 6 cm. Instead of modeling air gaps, screw holes, and complex leaf heads, “effective densities” are assigned to: 1) thin leaves, 2) thick leaves, 3) HT-, and 4) LT-interleaves. Results: The new MLC model is used to calculate dose profiles for Closed-MLC and Tongue-and-Groove fields at 5 cm depth for 6, 10 and 15 MV Varian beams. Calculations are compared with 1) Pin-point ionization chamber transmission ratios and 2) EBT3 Radiochromic films. Pinpoint readings were acquired beneath thick and thin leaves, and HT and LT interleaves. The best fit of measured dose profiles was obtained for the following parameters: Thick-leaf density = 16.1 g/cc, Thin-leaf density = 17.2 g/cc; HT Interleaf density = 12.4 g/cc, LT Interleaf density = 14.3 g/cc; Interleaf thickness = 1.1 mm. Attached figures show comparison of calculated and measured transmission ratios for the 3 energies. Note this is the only study where transmission profiles are compared with measurements for 3 different energies. Conclusion: The new MLC model reproduces transmission measurements within 0.1%. The next step is to implement the MLC model for real plans and quantify the improvement in dose calculation accuracy gained using this model for IMRT plans with high modulation factors.« less
  • Purpose: To develop a general method for human tissue characterization with dual-and multi-energy CT and evaluate its performance in determining elemental compositions and the associated proton stopping power relative to water (SPR) and photon mass absorption coefficients (EAC). Methods: Principal component analysis is used to extract an optimal basis of virtual materials from a reference dataset of tissues. These principal components (PC) are used to perform two-material decomposition using simulated DECT data. The elemental mass fraction and the electron density in each tissue is retrieved by measuring the fraction of each PC. A stoichiometric calibration method is adapted to themore » technique to make it suitable for clinical use. The present approach is compared with two others: parametrization and three-material decomposition using the water-lipid-protein (WLP) triplet. Results: Monte Carlo simulations using TOPAS for four reference tissues shows that characterizing them with only two PC is enough to get a submillimetric precision on proton range prediction. Based on the simulated DECT data of 43 references tissues, the proposed method is in agreement with theoretical values of protons SPR and low-kV EAC with a RMS error of 0.11% and 0.35%, respectively. In comparison, parametrization and WLP respectively yield RMS errors of 0.13% and 0.29% on SPR, and 2.72% and 2.19% on EAC. Furthermore, the proposed approach shows potential applications for spectral CT. Using five PC and five energy bins reduces the SPR RMS error to 0.03%. Conclusion: The proposed method shows good performance in determining elemental compositions from DECT data and physical quantities relevant to radiotherapy dose calculation and generally shows better accuracy and unbiased results compared to reference methods. The proposed method is particularly suitable for Monte Carlo calculations and shows promise in using more than two energies to characterize human tissue with CT.« less
  • Purpose: Monte Carlo (MC) simulation is typically regarded as the most accurate dose calculation method for proton therapy. Yet for real clinical cases, the overall accuracy also depends on that of the MC beam model. Commissioning a beam model to faithfully represent a real beam requires finely tuning a set of model parameters, which could be tedious given the large number of pencil beams to commmission. This abstract reports an automatic beam-model commissioning method for pencil-beam scanning proton therapy via an optimization approach. Methods: We modeled a real pencil beam with energy and spatial spread following Gaussian distributions. Mean energy,more » and energy and spatial spread are model parameters. To commission against a real beam, we first performed MC simulations to calculate dose distributions of a set of ideal (monoenergetic, zero-size) pencil beams. Dose distribution for a real pencil beam is hence linear superposition of doses for those ideal pencil beams with weights in the Gaussian form. We formulated the commissioning task as an optimization problem, such that the calculated central axis depth dose and lateral profiles at several depths match corresponding measurements. An iterative algorithm combining conjugate gradient method and parameter fitting was employed to solve the optimization problem. We validated our method in simulation studies. Results: We calculated dose distributions for three real pencil beams with nominal energies 83, 147 and 199 MeV using realistic beam parameters. These data were regarded as measurements and used for commission. After commissioning, average difference in energy and beam spread between determined values and ground truth were 4.6% and 0.2%. With the commissioned model, we recomputed dose. Mean dose differences from measurements were 0.64%, 0.20% and 0.25%. Conclusion: The developed automatic MC beam-model commissioning method for pencil-beam scanning proton therapy can determine beam model parameters with satisfactory accuracy.« less