Electron tracks simulation in water: Performance comparison between GPU CPU and the EUMED grid installation.

PURPOSE: We explored different technologies to minimize simulation time of the Monte-Carlo method for track generation following the Geant4-DNA processes for electrons in water. METHODS: A GPU software tool is developed for electron track simulations. A similar CPU version is also developed using the same collision models. CPU simulations were carried out on a single user desktop computer and on the computing grid France Grilles using 10 and 100 computing nodes. Computing time results for CPU, GPU, and grid simulations are compared with those using Geant4-DNA processes. RESULTS: The CPU simulations better performs when the number of electrons is less than 104 with 100 eV initial energy, this number decreases as the energy increases. The GPU simulations gives better results when the number of electrons is more than 104 with initial energy of 100 eV, this number decreases to 103 for electrons with 10KeV and increases back with higher energy. The use of the grid introduces an additional queuing time which slows down the overall simulation performance. Thus, the Grid gives better performance when the number of electrons is over 105 with initial energy of 10KeV, and this number decreases as the energy increases. CONCLUSIONS: The CPU is best suited for small numbers of primary incident electrons. The GPU is best suited when the number of primary incident particles occupies sufficient resources on GPU card in order to get an important computing power. The grid is best suited for simulations with high number of primary incident electrons with high initial energy.

Effects of a metallic implant on radiotherapy planning treatment-experience on a human cadaver.

BACKGROUND: Metallic implants (MIs) complicate radiotherapy planning. Several studies have worked on tissue-equivalent phantoms as experimental models to estimate dose distributions in this context. The application of these results to clinical practice remains disputable because the inhomogeneity of human tissue densities is a difficult factor to integrate into dose calculation software. In this work, we evaluate the impact of human tissue inhomogeneities by assessing the discrepancies between treatment planning system (TPS) dose calculations and measured delivered doses on a human cadaver with hip prostheses. METHODS: A total of 143 alanine dosimeters were positioned in contact with the prostheses (bones group), soft tissues (soft tissues group), skin surfaces (skin group) and natural cavities (cavities group) of a human cadaver. The planning target volume (PTV) corresponded to a standard endometrial cancer treatment. The irradiation was performed with 6 MV X-ray tomotherapy at the one fraction-dose of 10 Gy. RESULTS: A total of 140 dosimeters were analyzed. After applying a temperature correction coefficient to the measured doses, the global analysis of all dosimeters showed a significant difference between the calculated doses and the measured doses (P<0.001). For dosimeters of the bones, soft tissues, skin and cavities groups, this difference was also significant (P<0.001 for each group). The mean measured doses were 21.9% lower than the mean calculated doses in the global analysis and 17.0%, 21.2%, 33.0% and 19.0% lower for the bones, soft tissues, skin and cavities groups, respectively. CONCLUSIONS: This study showed that the received doses were significantly lower than the calculated doses and suggested the need to improve the understanding of this discrepancy.

Analytical, experimental, and Monte Carlo system response matrix for pinhole SPECT reconstruction.

PURPOSE: To assess the performance of two approaches to the system response matrix (SRM) calculation in pinhole single photon emission computed tomography (SPECT) reconstruction. METHODS: Evaluation was performed using experimental data from a low magnification pinhole SPECT system that consisted of a rotating flat detector with a monolithic scintillator crystal. The SRM was computed following two approaches, which were based on Monte Carlo simulations (MC-SRM) and analytical techniques in combination with an experimental characterization (AE-SRM). The spatial response of the system, obtained by using the two approaches, was compared with experimental data. The effect of the MC-SRM and AE-SRM approaches on the reconstructed image was assessed in terms of image contrast, signal-to-noise ratio, image quality, and spatial resolution. To this end, acquisitions were carried out using a hot cylinder phantom (consisting of five fillable rods with diameters of 5, 4, 3, 2, and 1 mm and a uniform cylindrical chamber) and a custom-made Derenzo phantom, with center-to-center distances between adjacent rods of 1.5, 2.0, and 3.0 mm. RESULTS: Good agreement was found for the spatial response of the system between measured data and results derived from MC-SRM and AE-SRM. Only minor differences for point sources at distances smaller than the radius of rotation and large incidence angles were found. Assessment of the effect on the reconstructed image showed a similar contrast for both approaches, with values higher than 0.9 for rod diameters greater than 1 mm and higher than 0.8 for rod diameter of 1 mm. The comparison in terms of image quality showed that all rods in the different sections of a custom-made Derenzo phantom could be distinguished. The spatial resolution (FWHM) was 0.7 mm at iteration 100 using both approaches. The SNR was lower for reconstructed images using MC-SRM than for those reconstructed using AE-SRM, indicating that AE-SRM deals better with the projection noise than MC-SRM. CONCLUSIONS: The authors' findings show that both approaches provide good solutions to the problem of calculating the SRM in pinhole SPECT reconstruction. The AE-SRM was faster to create and handle the projection noise better than MC-SRM. Nevertheless, the AE-SRM required a tedious experimental characterization of the intrinsic detector response. Creation of the MC-SRM required longer computation time and handled the projection noise worse than the AE-SRM.Nevertheless, the MC-SRM inherently incorporates extensive modeling of the system and therefore experimental characterization was not required.

Geant4-based Monte Carlo simulations on GPU for medical applications.

Monte Carlo simulation (MCS) plays a key role in medical applications, especially for emission tomography and radiotherapy. However MCS is also associated with long calculation times that prevent its use in routine clinical practice. Recently, graphics processing units (GPU) became in many domains a low cost alternative for the acquisition of high computational power. The objective of this work was to develop an efficient framework for the implementation of MCS on GPU architectures. Geant4 was chosen as the MCS engine given the large variety of physics processes available for targeting different medical imaging and radiotherapy applications. In addition, Geant4 is the MCS engine behind GATE which is actually the most popular medical applications' simulation platform. We propose the definition of a global strategy and associated structures for such a GPU based simulation implementation. Different photon and electron physics effects are resolved on the fly directly on GPU without any approximations with respect to Geant4. Validations have shown equivalence in the underlying photon and electron physics processes between the Geant4 and the GPU codes with a speedup factor of 80-90. More clinically realistic simulations in emission and transmission imaging led to acceleration factors of 400-800 respectively compared to corresponding GATE simulations.