Validation of the MC-GPU Monte Carlo code against the PENELOPE/penEasy code system and benchmarking against experimental conditions for typical radiation qualities and setups in interventional radiology and cardiology

Bosman, David Fernández; Balcaza, Víctor García; Delgado, Clara; Principi, Sara; Duch, M.A.; Ginjaume, M.

doi:10.1016/j.ejmp.2021.01.075

Cited by 12 publications

(4 citation statements)

References 30 publications

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“…The X-ray interaction models and material properties have been adapted from PENELOPE [12]. The code was first developed by Badal et al 2009 [7], but, as has been already highlighted, it was upgraded and validated as described in a previous article [8].…”

Section: Pymcgpu-irmentioning

confidence: 99%

“…In a recently published paper MC-GPU was validated against the well-known PENELOPE/PenEasy code system and against experimental conditions for typical radiation qualities and set-ups in IR [8]. The results showed that MC-GPU is capable of simulating the underlying physical phenomena involved in IR procedures and also of calculating the doses in different tissues and organs in very short computation times (less than one minute per irradiation event).…”

Section: Introductionmentioning

confidence: 99%

“…The results showed that MC-GPU is capable of simulating the underlying physical phenomena involved in IR procedures and also of calculating the doses in different tissues and organs in very short computation times (less than one minute per irradiation event). From the perspective of the MEDIRAD Task 2.2.2 objective, and as a continuation of the already mentioned work [8], the next steps for the implementation of MC-GPU in clinics consist in the following. On the first hand, the improvement of MC-GPU to fulfil all the basic requirements needed by an SDC tool, such as automatization of all the steps needed for the dose calculation.…”

Section: Introductionmentioning

confidence: 99%

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Validation of organ dose calculations with PyMCGPU-IR in realistic interventional set-ups

et al. 2022

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Interventional radiology procedures are associated with high skin dose exposure. The 2013/59/ EURATOM Directive establishes that the equipment used for interventional radiology must have a device or a feature informing the practitioner of relevant parameters for assessing patient dose at the end of the procedure. This work presents and validates PyMCGPU-IR, a patient dose monitoring tool for interventional cardiology and radiology procedures based on MC-GPU. MC-GPU is a freely available Monte Carlo (MC) code of photon transport in a voxelized geometry which uses the computational power of commodity Graphics Processing Unit cards (GPU) to accelerate calculations. Methodologies: PyMCGPU-IR was validated against two different experimental set-ups. The first one consisted of skin dose measurements for different beam angulations on an adult Rando Alderson anthropomorphic phantom. The second consisted of organ dose measurements in three clinical procedures using the Rando Alderson phantom. Results:The results obtained for the skin dose measurements show differences below 6%. For the clinical procedures the differences are within 20% for most cases. Conclusions: PyMCGPU-IR offers both, high performance and accuracy for dose assessment when compared with skin and organ dose measurements. It also allows the calculation of dose values at specific positions and organs, the dose distribution and the location of the maximum doses per organ. In addition, PyMCGPU-IR overcomes the time limitations of CPU-based MC codes.

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Section: Pymcgpu-irmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Validation of organ dose calculations with PyMCGPU-IR in realistic interventional set-ups

et al. 2022

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“…MC‐GPU has already been employed for simulating some applications involving low‐energy (X‐ray) beams, for example, breast imaging studies and virtual clinical trials 10,11 and coherent X‐ray scattering 12 by adapting the code to include molecular interference 13 . Original and modified MC‐GPU codes were also validated for applications in interventional radiology and cardiology 14,15 . MC‐GPU was also adapted for patient‐specific CT dose calculations 16 .…”

Section: Introductionmentioning

confidence: 99%

Technical note: MC‐GPU breast dosimetry validations with other Monte Carlo codes and phase space file implementation

Massera

Thomson

2021

Medical Physics

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To validate the MC-GPU Monte Carlo (MC) code for dosimetric studies in X-ray breast imaging modalities: mammography, digital breast tomosynthesis, contrast enhanced digital mammography, and breast-CT. Moreover, to implement and validate a phase space file generation routine. Methods: The MC-GPU code (v. 1.5 DBT) was modified in order to generate phase space files and to be compatible with PENELOPE v. 2018 derived cross-section database. Simulations were performed with homogeneous and anthropomorphic breast phantoms for different breast imaging techniques. The glandular dose was computed for each case and compared with results from the PENELOPE (v. 2014) + penEasy (v. 2015) and egs_brachy (EGSnrc) MC codes. Afterward, several phase space files were generated with MC-GPU and the scored photon spectra were compared between the codes.The phase space files generated in MC-GPU were used in PENELOPE and EGSnrc to calculate the glandular dose, and compared with the original dose scored in MC-GPU. Results: MC-GPU showed good agreement with the other codes when calculating the glandular dose distribution for mammography, mean glandular dose for digital breast tomosynthesis, and normalized glandular dose for breast-CT. The latter case showed average/maximum relative differences of 2.3%/27%, respectively, compared to other literature works, with the larger differences observed at low energies (around 10 keV). The recorded photon spectra entering a voxel were similar (within statistical uncertainties) between the three MC codes. Finally, the reconstructed glandular dose in a voxel from a phase space file differs by less than 0.65%, with an average of 0.18%-0.22% between the different MC codes, agreement within approximately 2𝜎 statistical uncertainties. In some scenarios, the simulations performed in MC-GPU were from 20 up to 40 times faster than those performed by PENELOPE. Conclusions:The results indicate that MC-GPU code is suitable for breast dosimetric studies for different X-ray breast imaging modalities, with the advantage of a high performance derived from GPUs. The phase space file implementation was validated and is compatible with the IAEA standard, allowing multiscale MC simulations with a combination of CPU and GPU codes.

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XIORT‐MC: A real‐time MC‐based dose computation tool for low‐ energy X‐rays intraoperative radiation therapy

et al. 2021

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Purpose: The INTRABEAM system is a miniature accelerator for low-energy X-ray Intra-Operative Radiation Therapy (IORT), and it could benefit from a fast and accurate dose computation tool. With regards to accuracy, dose computed with Monte Carlo (MC) simulations are the gold standard, however, they require a large computational effort and consequently they are not suitable for realtime dose planning. This work presents a comparison of the implementation on Graphics Processing Unit (GPU) of two different dose calculation algorithms based on MC phase-space (PHSP) information to compute dose distributions for the INTRABEAM device within seconds and with the accuracy of realistic MC simulations. Methods: The MC-based algorithms we present incorporate photoelectric, Compton and Rayleigh effects for the interaction of low-energy X-rays. XIORT-MC (X-ray Intra-Operative Radiation Therapy Monte Carlo) includes two dose calculation algorithms; a Woodcock-based MC algorithm (WC-MC) and a Hybrid MC algorithm (HMC), and it is implemented in CPU and in GPU. Detailed MC simulations have been generated to validate our tool in homogeneous and heterogeneous conditions with all INTRABEAM applicators, including three clinically realistic CT-based simulations. A performance study has been done to determine the acceleration reached with the code, in both CPU and GPU implementations. Results: Dose distributions were obtained with the HMC and the WC-MC and compared to standard reference MC simulations with more than 95% voxels fulfilling a 7%-0.5 mm gamma evaluation in all the cases considered. The CPU-HMC is 100 times more efficient than the reference MC, and the CPU-WC-MC is about 50 times more efficient. With the GPU implementation, the particle tracking of the WC-MC is faster than the HMC, with the extraction of the particle's information from the PHSP file taking a major part of the time. However, thanks to the variance reduction techniques implemented in the HMC, up to 400 times less particles are needed in the HMC to reach the same level of noise than the WC-MC. Therefore, in our implementation for INTRABEAM energies, the HMC is about 1.3 times more efficient than the WC-MC in an NVIDIA GeForce GTX 1080 Ti card and about 5.5 times more efficient in an NVIDIA GeForce RTX 3090. Dose with noise below 5% has been obtained in realistic situations in less than 5 s with the WC-MC and in less than 0.5 s with the HMC. Conclusions:The XIORT-MC is a dose computation tool designed to take full advantage of modern GPUs, making possible to obtain MC-grade accurate dose distributions within seconds. Its high speed allows a real-time dose calculation that includes the realistic effects of the beam in voxelized geometries

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Validation of the MC-GPU Monte Carlo code against the PENELOPE/penEasy code system and benchmarking against experimental conditions for typical radiation qualities and setups in interventional radiology and cardiology

Cited by 12 publications

References 30 publications

Validation of organ dose calculations with PyMCGPU-IR in realistic interventional set-ups

Validation of organ dose calculations with PyMCGPU-IR in realistic interventional set-ups

Technical note: MC‐GPU breast dosimetry validations with other Monte Carlo codes and phase space file implementation

XIORT‐MC: A real‐time MC‐based dose computation tool for low‐ energy X‐rays intraoperative radiation therapy

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