The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Accelerating Monte Carlo simulations of photon transport in a voxelized geometry using a massively parallel graphics processing unit
Abstract
Purpose: It is a known fact that Monte Carlo simulations of radiation transport are computationally intensive and may require long computing times. The authors introduce a new paradigm for the acceleration of Monte Carlo simulations: The use of a graphics processing unit (GPU) as the main computing device instead of a central processing unit (CPU). Methods: A GPU‐based Monte Carlo code that simulates photon transport in a voxelized geometry with the accurate physics models from PENELOPE has been developed using the CUDA ™ programming model (NVIDIA Corporation, Santa Clara, CA). Results: An outline of the new code and a sample x‐ray imaging simulation with an anthropomorphic phantom are presented. A remarkable 27‐fold speed up factor was obtained using a GPU compared to a single core CPU. Conclusions: The reported results show that GPUs are currently a good alternative to CPUs for the simulation of radiation transport. Since the performance of GPUs is currently increasing at a faster pace than that of CPUs, the advantages of GPU‐based software are likely to be more pronounced in the future.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Monte Carlo simulation is prohibitively expensive for training-set-size DRR generation on conventional hardware.
“Unfortunately, for training-set-size DRR generation on conventional hardware, MC is prohibitively expensive.”
From DeepDRR – A Catalyst for Machine Learning in Fluoroscopy-Guided Procedures (2018) · cites this paper as [11]
Accelerated MC simulation on an NVIDIA Titan Xp takes approximately 4 hours for a single X-ray image with 10^10 photons.
“accelerated MC simulation [11] on an NVIDIA Titan Xp takes ≈ 4 hours for a single X-ray image with 10 10 photons.”
From DeepDRR – A Catalyst for Machine Learning in Fluoroscopy-Guided Procedures (2018) · cites this paper as [11]
Citations
Cites
PENELOPE 2014: A code system for Monte Carlo simulation of electron and photon transport
Nuclear Energy Agency (2015) · Penelope · doi:10.1787/4e3f14db-en · Cited by 537
Alerstam, E., Svensson, T., & Andersson‐Engels, S. (2008) · Journal of Biomedical Optics · doi:10.1117/1.3041496 · Cited by 410
GPU accelerated Monte Carlo simulation of the 2D and 3D Ising model
Preis, T., Virnau, P., Paul, W., & Schneider, J. (2009) · Journal of Computational Physics · doi:10.1016/j.jcp.2009.03.018 · Cited by 330
Sempau, J., Wilderman, S. J., & Bielajew, A. F. (2000) · Physics in Medicine and Biology · doi:10.1088/0031-9155/45/8/315 · Cited by 323
A package of Linux scripts for the parallelization of Monte Carlo simulations
Badal, A., & Sempau, J. (2006) · Computer Physics Communications · doi:10.1016/j.cpc.2006.05.009 · Cited by 82
Self-citationBadano, A. G., & Sempau, J. (2006) · Physics in Medicine and Biology · doi:10.1088/0031-9155/51/6/013 · Cited by 80
Self-citationBenchmark of PENELOPE code for low-energy photon transport: dose comparisons with MCNP4 and EGS4
Ye, S., Brezovich, I. A., Pareek, P. N., et al. (2004) · Physics in Medicine and Biology · doi:10.1088/0031-9155/49/3/003 · Cited by 69
Fast convolution‐superposition dose calculation on graphics hardware
Hissoiny, S., Ozell, B., & Després, P. (2009) · Medical Physics · doi:10.1118/1.3120286 · Cited by 56
Fast Monte Carlo calculation of scatter corrections for CBCT images
Mainegra‐Hing, E., & Kawrakow, I. (2008) · Journal of Physics Conference Series · doi:10.1088/1742-6596/102/1/012017 · Cited by 55
Dose calculation validation of VMC++ for photon beams
Gardner, J., Siebers, J. V., & Kawrakow, I. (2007) · Medical Physics · doi:10.1118/1.2714473 · Cited by 38
Badal, A., Kyprianou, I. S., Badano, A. G., et al. (2007) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2007.11.009 · Cited by 20
Self-citationStream processors: a new platform for Monte Carlo calculations
Després, P., Rinkel, J., Hasegawa, B. H., et al. (2008) · Journal of Physics Conference Series · doi:10.1088/1742-6596/102/1/012007 · Cited by 14
Flagged first, then by citations
Research works printed but not linked: 1
- Christ A., The Virtual Family—Development of anatomical CAD models of two adults and two children for dosimetric simulations, Phys. Med. Biol.
Cited by
Sorted by citations
Badano, A., Graff, C. G., Badal, A., Sharma, D., Zeng, R., Samuelson, F. W., Glick, S. J., & Myers, K. J. (2018) · JAMA Network Open · doi:10.1001/jamanetworkopen.2018.5474 · Cited by 235
Self-citationDeepDRR – A Catalyst for Machine Learning in Fluoroscopy-Guided Procedures
Unberath, M., Zaech, J.-N., Lee, S. C., Bier, B., Fotouhi, J., Armand, M., & Navab, N. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-00937-3_12 · Cited by 145
“The network was trained on 330 images generated via MC simulation [11], augmented by random rotations and reflections.”
BackgroundMethodInfluentialSisniega, A., Zbijewski, W., Badal, A., Kyprianou, I. S., Stayman, J. W., Vaquero, J. J., & Siewerdsen, J. H. (2013) · Medical Physics · doi:10.1118/1.4801895 · Cited by 117
Self-citation
Metadata
- Authors
- Badal, A., & Badano, A.
- Affiliations
- United States Food and Drug Administration (US)
- Corresponding author
- Badal, Andreu
- Venue
- Medical Physics
- Publisher
- Wiley
- Published
- 2009-10-02
- Volume
- 36
- Issue
- 11
- Pages
- 4878-4880
- ISSN
- 0094-2405 (ISSN-L)
- 2473-4209
- OpenAlex
- W2029927888
- Cited by
- 358
- References
- 12
- Citations by year
- 2026: 12 · 2025: 40 · 2024: 23 · 2023: 25 · 2022: 25 · 2021: 34 · 2020: 27 · 2019: 21 · 2018: 12 · 2017: 21 · 2016: 20 · 2015: 16 · 2014: 19 · 2013: 19 · 2012: 19
- FWCI
- 22.87
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Medical Physics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Computer Graphics and Visualization Techniques
- Keywords
- GPU acceleration, Monte Carlo photon transport, radiation transport simulation, Penelope, CUDA, X-ray imaging simulation, anthropomorphic phantom, speedup, massively parallel computing
- MeSH terms
- Algorithms, Computer Simulation, Computers, Electronics, Humans, Models, Anatomic, Monte Carlo Method, Scattering, Radiation, Software, Time Factors, X-Rays, Photons, Phantoms, Imaging
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
