Benoît Ozell

GPUMCD: A new GPU‐oriented Monte Carlo dose calculation platform

PURPOSE Monte Carlo methods are considered as the gold standard for dosimetric computations in radiotherapy. Their execution time is, however, still an obstacle to the routine use of Monte Carlo packages in a clinical setting. To address this problem, a completely new, and designed from the ground up for the GPU, Monte Carlo dose calculation package for voxelized geometries is proposed: GPUMCD. METHOD GPUMCD implements a coupled photon-electron Monte Carlo simulation for energies in the range of 0.01-20 MeV. An analog simulation of photon interactions is used and a class II condensed history method has been implemented for the simulation of electrons. A new GPU random number generator, some divergence reduction methods, as well as other optimization strategies are also described. GPUMCD was run on a NVIDIA GTX480, while single threaded implementations of EGSnrc and DPM were run on an Intel Core i7 860. RESULTS Dosimetric results obtained with GPUMCD were compared to EGSnrc. In all but one test case, 98% or more of all significant voxels passed the gamma criteria of 2%-2 mm. In terms of execution speed and efficiency, GPUMCD is more than 900 times faster than EGSnrc and more than 200 times faster than DPM, a Monte Carlo package aiming fast executions. Absolute execution times of less than 0.3 s are found for the simulation of 1M electrons and 4M photons in water for monoenergetic beams of 15 MeV, including GPU-CPU memory transfers. CONCLUSION GPUMCD, a new GPU-oriented Monte Carlo dose calculation platform, has been compared to EGSnrc and DPM in terms of dosimetric results and execution speed. Its accuracy and speed make it an interesting solution for full Monte Carlo dose calculation in radiation oncology.

Fast convolution-superposition dose calculation on graphics hardware.

The numerical calculation of dose is central to treatment planning in radiation therapy and is at the core of optimization strategies for modern delivery techniques. In a clinical environment, dose calculation algorithms are required to be accurate and fast. The accuracy is typically achieved through the integration of patient-specific data and extensive beam modeling, which generally results in slower algorithms. In order to alleviate execution speed problems, the authors have implemented a modern dose calculation algorithm on a massively parallel hardware architecture. More specifically, they have implemented a convolution-superposition photon beam dose calculation algorithm on a commodity graphics processing unit (GPU). They have investigated a simple porting scenario as well as slightly more complex GPU optimization strategies. They have achieved speed improvement factors ranging from 10 to 20 times with GPU implementations compared to central processing unit (CPU) implementations, with higher values corresponding to larger kernel and calculation grid sizes. In all cases, they preserved the numerical accuracy of the GPU calculations with respect to the CPU calculations. These results show that streaming architectures such as GPUs can significantly accelerate dose calculation algorithms and let envision benefits for numerically intensive processes such as optimizing strategies, in particular, for complex delivery techniques such as IMRT and are therapy.

A convolution-superposition dose calculation engine for GPUs

PURPOSE Graphic processing units (GPUs) are increasingly used for scientific applications, where their parallel architecture and unprecedented computing power density can be exploited to accelerate calculations. In this paper, a new GPU implementation of a convolution/superposition (CS) algorithm is presented. METHODS This new GPU implementation has been designed from the ground-up to use the graphics cards strengths and to avoid its weaknesses. The CS GPU algorithm takes into account beam hardening, off-axis softening, kernel tilting, and relies heavily on raytracing through patient imaging data. Implementation details are reported as well as a multi-GPU solution. RESULTS An overall single-GPU acceleration factor of 908x was achieved when compared to a nonoptimized version of the CS algorithm implemented in PlanUNC in single threaded central processing unit (CPU) mode, resulting in approximatively 2.8 s per beam for a 3D dose computation on a 0.4 cm grid. A comparison to an established commercial system leads to an acceleration factor of approximately 29x or 0.58 versus 16.6 s per beam in single threaded mode. An acceleration factor of 46x has been obtained for the total energy released per mass (TERMA) calculation and a 943x acceleration factor for the CS calculation compared to PlanUNC. Dose distributions also have been obtained for a simple water-lung phantom to verify that the implementation gives accurate results. CONCLUSIONS These results suggest that GPUs are an attractive solution for radiation therapy applications and that careful design, taking the GPU architecture into account, is critical in obtaining significant acceleration factors. These results potentially can have a significant impact on complex dose delivery techniques requiring intensive dose calculations such as intensity-modulated radiation therapy (IMRT) and arc therapy. They also are relevant for adaptive radiation therapy where dose results must be obtained rapidly.

Automatic semantic web annotation of named entities

This paper describes a method to perform automated semantic annotation of named entities contained in large corpora. The semantic annotation is made in the context of the Semantic Web. The method is based on an algorithm that compares the set of words that appear before and after the name entity with the content of Wikipedia articles, and identifies the more relevant one by means of a similarity measure. It then uses the link that exists between the selected Wikipedia entry and the corresponding RDF description in the Linked Data project to establish a connection between the named entity and some URI in the Semantic Web. We present our system, discuss its architecture, and describe an algorithm dedicated to ontological disambiguation of named entities contained in large-scale corpora. We evaluate the algorithm, and present our results.

Fast dose calculation in magnetic fields with GPUMCD.

A new hybrid imaging-treatment modality, the MRI-Linac, involves the irradiation of the patient in the presence of a strong magnetic field. This field acts on the charged particles, responsible for depositing dose, through the Lorentz force. These conditions require a dose calculation engine capable of taking into consideration the effect of the magnetic field on the dose distribution during the planning stage. Also in the case of a change in anatomy at the time of treatment, a fast online replanning tool is desirable. It is improbable that analytical solutions such as pencil beam calculations can be efficiently adapted for dose calculations within a magnetic field. Monte Carlo simulations have therefore been used for the computations but the calculation speed is generally too slow to allow online replanning. In this work, GPUMCD, a fast graphics processing unit (GPU)-based Monte Carlo dose calculation platform, was benchmarked with a new feature that allows dose calculations within a magnetic field. As a proof of concept, this new feature is validated against experimental measurements. GPUMCD was found to accurately reproduce experimental dose distributions according to a 2%-2 mm gamma analysis in two cases with large magnetic field-induced dose effects: a depth-dose phantom with an air cavity and a lateral-dose phantom surrounded by air. Furthermore, execution times of less than 15 s were achieved for one beam in a prostate case phantom for a 2% statistical uncertainty while less than 20 s were required for a seven-beam plan. These results indicate that GPUMCD is an interesting candidate, being fast and accurate, for dose calculations for the hybrid MRI-Linac modality.

Sub-second high dose rate brachytherapy Monte Carlo dose calculations with bGPUMCD.

PURPOSE To establish the accuracy and speed ofbGPUMCD, a GPU-oriented Monte Carlo code used for high dose rate brachytherapy dose calculations. The first objective is to evaluate the time required for dose calculation when full Monte Carlo generated dose distribution kernels are used for plan optimization. The second objective is to assess the accuracy and speed when recalculating pre-optimized plans, consisting of many dwell positions. METHODS bGPUMCD is tested with three clinical treatment plans : one prostate case, one breast case, and one rectum case with a shielded applicator. Reference distributions, generated with GEANT4, are used as a basis of comparison. Calculations of full dose distributions of pre-optimized treatment plans as well as single dwell dosimetry are performed. Single source dosimetry, based on TG-43 parameters reproduction, is also presented for the microSelectron V2 (Nucletron, Veenendaal, The Netherlands). RESULTS In timing experiments, the computation of single dwell position dose kernels takes between 0.25 and 0.5 s.bGPUMCD can compute full dose distributions of previously optimized plans in ∼2 s. bGPUMCD is capable of computing pre-optimized brachytherapy plans within 1% for the prostate case and 2% for the breast and shielded applicator cases, when comparing the dosimetric parameters D90 and V100 of the reference (GEANT4) and bGPUMCD distributions. For all voxels within the target, an absolute average difference of approximately 1% is found for the prostate case, less than 2% for the breast case and less than 2% for the rectum case with shielded applicator. Larger point differences (>5%) are found within bony regions in the prostate case, where bGPUMCD underdoses compared to GEANT4. Single source dosimetry results are mostly within 2% for the radial function and within 1%-4% for the anisotropic function. CONCLUSIONS bGPUMCD has the potential to allow for fast MC dose calculation in a clinical setting for all phases of HDR treatment planning, from dose kernel calculations for plan optimization to plan recalculation.PURPOSE To establish the accuracy and speed of bGPUMCD, a GPU-oriented Monte Carlo code used for high dose rate brachytherapy dose calculations. The first objective is to evaluate the time required for dose calculation when full Monte Carlo generated dose distribution kernels are used for plan optimization. The second objective is to assess the accuracy and speed when recalculating pre-optimized plans, consisting of many dwell positions. METHODS bGPUMCD is tested with three clinical treatment plans : one prostate case, one breast case, and one rectum case with a shielded applicator. Reference distributions, generated with GEANT4, are used as a basis of comparison. Calculations of full dose distributions of pre-optimized treatment plans as well as single dwell dosimetry are performed. Single source dosimetry, based on TG-43 parameters reproduction, is also presented for the microSelectron V2 (Nucletron, Veenendaal, The Netherlands). RESULTS In timing experiments, the computation of single dwell position dose kernels takes between 0.25 and 0.5 s. bGPUMCD can compute full dose distributions of previously optimized plans in ∼2 s. bGPUMCD is capable of computing pre-optimized brachytherapy plans within 1% for the prostate case and 2% for the breast and shielded applicator cases, when comparing the dosimetric parameters D90 and V100 of the reference (GEANT4) and bGPUMCD distributions. For all voxels within the target, an absolute average difference of approximately 1% is found for the prostate case, less than 2% for the breast case and less than 2% for the rectum case with shielded applicator. Larger point differences (>5%) are found within bony regions in the prostate case, where bGPUMCD underdoses compared to GEANT4. Single source dosimetry results are mostly within 2% for the radial function and within 1%-4% for the anisotropic function. CONCLUSIONS bGPUMCD has the potential to allow for fast MC dose calculation in a clinical setting for all phases of HDR treatment planning, from dose kernel calculations for plan optimization to plan recalculation.

Validation of GPUMCD for low-energy brachytherapy seed dosimetry.

PURPOSE To validate GPUMCD, a new package for fast Monte Carlo dose calculations based on the GPU (graphics processing unit), as a tool for low-energy single seed brachytherapy dosimetry for specific seed models. As the currently accepted method of dose calculation in low-energy brachytherapy computations relies on severe approximations, a Monte Carlo based approach would result in more accurate dose calculations, taking in to consideration the patient anatomy as well as interseed attenuation. The first step is to evaluate the capability of GPUMCD to reproduce low-energy, single source, brachytherapy calculations which could ultimately result in fast and accurate, Monte Carlo based, brachytherapy dose calculations for routine planning. METHODS A mixed geometry engine was integrated to GPUMCD capable of handling parametric as well as voxelized geometries. In order to evaluate GPUMCD for brachytherapy calculations, several dosimetry parameters were computed and compared to values found in the literature. These parameters, defined by the AAPM Task-Group No. 43, are the radial dose function, the 2D anisotropy function, and the dose rate constant. These three parameters were computed for two different brachytherapy sources: the Amersham OncoSeed 6711 and the Imagyn IsoStar IS-12501. RESULTS GPUMCD was shown to yield dosimetric parameters similar to those found in the literature. It reproduces radial dose functions to within 1.25% for both sources in the 0.5< r <10 cm range. The 2D anisotropy function was found to be within 3% at r =5 cm and within 4% at r = 1 cm. The dose rate constants obtained were within the range of other values reported in the literature. CONCLUSION GPUMCD was shown to be able to reproduce various TG-43 parameters for two different low-energy brachytherapy sources found in the literature. The next step is to test GPUMCD as a fast clinical Monte Carlo brachytherapy dose calculations with multiple seeds and patient geometry, potentially providing more accurate results than the TG-43 formalism while being much faster than calculations using general purpose Monte Carlo codes.

Analysis and visualization tools in CFD, part I: a configurable data extraction environment

Abstract The objective is to present new ideas for the implementation of visualization and analysis environments. This is carried out with a software interface allowing the design and configuration of project-specific environments built around a core library that performs all the data extraction and manipulation. This includes processing of solutions for rendering as well as for extraction of flow features for active control on the solution procedure. While the first function is highly interactive, the second is usually a batch oriented process coupled to the numerical algorithm. To achieve this goal, it is necessary to interpret solution data in a generic sense. The grid, the primary variables of the problem, and the derived variables are all considered as a number of simple scalar (discrete) fields defined on the domain. Basic entities in the library then provide building blocks to create images for rendering purposes or new fields as required in the grid adaption control loop. A data analysis language has been created. Its entities and grammar are oriented towards the description of both the rendering and the analysis processes. This customized environment is saved in an user configuration file, with an easily understandable syntax, which is loaded at execution time, hence providing the proper configuration for each application.

Fast GPU-based Monte Carlo simulations for LDR prostate brachytherapy

The aim of this study was to evaluate the potential of bGPUMCD, a Monte Carlo algorithm executed on Graphics Processing Units (GPUs), for fast dose calculations in permanent prostate implant dosimetry. It also aimed to validate a low dose rate brachytherapy source in terms of TG-43 metrics and to use this source to compute dose distributions for permanent prostate implant in very short times. The physics of bGPUMCD was reviewed and extended to include Rayleigh scattering and fluorescence from photoelectric interactions for all materials involved. The radial and anisotropy functions were obtained for the Nucletron SelectSeed in TG-43 conditions. These functions were compared to those found in the MD Anderson Imaging and Radiation Oncology Core brachytherapy source registry which are considered the TG-43 reference values. After appropriate calibration of the source, permanent prostate implant dose distributions were calculated for four patients and compared to an already validated Geant4 algorithm. The radial function calculated from bGPUMCD showed excellent agreement (differences within 1.3%) with TG-43 accepted values. The anisotropy functions at r = 1 cm and r = 4 cm were within 2% of TG-43 values for angles over 17.5°. For permanent prostate implants, Monte Carlo-based dose distributions with a statistical uncertainty of 1% or less for the target volume were obtained in 30 s or less for 1 × 1 × 1 mm(3) calculation grids. Dosimetric indices were very similar (within 2.7%) to those obtained with a validated, independent Monte Carlo code (Geant4) performing the calculations for the same cases in a much longer time (tens of minutes to more than a hour). bGPUMCD is a promising code that lets envision the use of Monte Carlo techniques in a clinical environment, with sub-minute execution times on a standard workstation. Future work will explore the use of this code with an inverse planning method to provide a complete Monte Carlo-based planning solution.

Virtual reality simulator for scoliosis surgery training: Transatlantic collaborative tests

Scoliosis is a complex deformation of the spine requiring, in severe cases, a highly delicate and invasive surgical instrumentation operation to correct the spinal deformities. Available traditional tools for surgical training have major drawbacks for which virtual reality (VR) technologies and computer simulation can offer solutions. In this paper, we introduce a surgical simulator integrating a complex patient-specific biomechanical model into a VR immersive environment in a collaborative context, the first of its kind for scoliosis surgery training. We present the results for the fully collaborative AVE (audio visual environment) aspects of the simulator. Haptic forces are computed in the biomechanical model, but not yet available as a haptic feedback because of the high forces and torques characteristic of scoliosis surgery, requiring the use of a specifically designed haptic device (in progress). Transatlantic collaborative tests showed that, with our simulator, users on different continents can train collaboratively for scoliosis surgery and visualise the forces and the resulting correction. With the eventual addition of haptic devices, they will also be able to feel the forces remotely.