Initial development of goCMC: a GPU-oriented fast cross-platform Monte Carlo engine for carbon ion therapy

Abstract

Monte Carlo (MC) simulation is considered as the most accurate method for calculation of absorbed dose and fundamental physics quantities related to biological effects in carbon ion therapy. To improve its computational efficiency, we have developed a GPU-oriented fast MC package named goCMC, for carbon therapy. goCMC simulates particle transport in voxelized geometry with kinetic energy up to 450 MeV/u. Class II condensed history simulation scheme with a continuous slowing down approximation was employed. Energy straggling and multiple scattering were modeled. δ-electrons were terminated with their energy locally deposited. Four types of nuclear interactions were implemented in goCMC, i.e., carbon-hydrogen, carbon-carbon, carbon-oxygen and carbon-calcium inelastic collisions. Total cross section data from Geant4 were used. Secondary particles produced in these interactions were sampled according to particle yield with energy and directional distribution data derived from Geant4 simulation results. Secondary charged particles were transported following the condensed history scheme, whereas secondary neutral particles were ignored. goCMC was developed under OpenCL framework and is executable on different platforms, e.g. GPU and multi-core CPU. We have validated goCMC with Geant4 in cases with different beam energy and phantoms including four homogeneous phantoms, one heterogeneous half-slab phantom, and one patient case. For each case 3 × 107 carbon ions were simulated, such that in the region with dose greater than 10% of maximum dose, the mean relative statistical uncertainty was less than 1%. Good agreements for dose distributions and range estimations between goCMC and Geant4 were observed. 3D gamma passing rates with 1%/1 mm criterion were over 90% within 10%) isodose line except in two extreme cases, and those with 2%/1 mm criterion were all over 96%. Efficiency and code portability were tested with different GPUs and CPUs. Depending on the beam energy and voxel size, the computation time to simulate 107 carbons was 9.9–125 sec, 2.5–50 sec and 60–612 sec on an AMD Radeon GPU card, an NVidia GeForce GTX 1080 GPU card and an Intel Xeon E5-2640 CPU, respectively. The combined accuracy, efficiency and portability make goCMC attractive for research and clinical applications in carbon ion therapy.

Dosimetric evaluation of 4D‐CBCT reconstructed by Simultaneous Motion Estimation and Image Reconstruction (SMEIR) for carbon ion therapy of lung cancer

“…The goCMC code simulates carbon ion transport, through a voxelized volume with kinetic energy up to 450 , using total cross‐sectional data from Geant4. Validation of goCMC against Geant4 has been shown for different beam energies and by conducting experiments in phantoms in addition to actual patients …”

“…Recent success in the use of graphical processing units (GPU) in Monte Carlo simulation has been exploited to develop a complete MC engine called goCMC (GPU OpenCL Carbon Monte Carlo) for carbon ion therapy, which includes features like source modeling and RBE calculation. 25 The goCMC code simulates carbon ion transport, through a voxelized volume with kinetic energy up to 450 MeVu À1 , using total cross-sectional data from Geant4. Validation of goCMC against Geant4 has been shown for different beam energies and by conducting experiments in phantoms in addition to actual patients.…”

Report of a National Cancer Institute special panel: Characterization of the physical parameters of particle beams for biological research

“…These include FLUKA, Geant4, MCNPX, SHIELD‐HIT, and PHITS . Other codes, specialized for heavy ion dose calculation have been developed as well …”

“…89,90 Other codes, specialized for heavy ion dose calculation have been developed as well. 91 For the most part, there is very little difference in the handling of particle tracking among the codes as they are all based on condensed history algorithms. The differences among codes are mainly in the underlying physics and secondary particle production.…”

Advanced Treatment Planning

“…This latter means that the next generation treatment planning systems should incorporate the handling of the uncertainties related with the beam delivery, patient positions, RBE models, etc. GPU‐based TPS could represent a promising solution of these problems . The speed of dose and RBE computations is also a key prerequisite for advanced treatment planning tools such as multicriteria and automatic optimization, and robust optimization, where linearity in dose addition is frequently exploited to speed up calculations; however, this linearity is not given for RBE‐weighted particle dose distributions.…”