X-ray simulations with gVXR in education, digital twining, experiment planning, and data analysis

gVirtualXray (gVXR) is an open-source framework that relies on the Beer–Lambert law to simulate X-ray images in real time on a graphics processor unit (GPU) using triangular meshes. A wide range of programming languages is supported (C/C++, Python, R, Ruby, Tcl, C#, Java, and GNU Octave). Simulations generated with gVXR have been benchmarked with clinically realistic phantoms (i.e. complex structures and materials) using Monte Carlo (MC) simulations, real radiographs and real digitally reconstructed radiographs (DRRs), and X-ray computed tomography (XCT). It has been used in a wide range of applications, including real-time medical simulators, proposing a new densitometric radiographic modality in clinical imaging, studying noise removal techniques in fluoroscopy, teaching particle physics and X-ray imaging to undergraduate students in engineering, and XCT to masters students, predicting image quality and artifacts in material science, etc. gVXR has also been used to produce a high number of realistic simulated images in optimisation problems and to train machine learning algorithms. This paper presents a comprehensive review of such applications of gVXR. • gVXR: open-source framework relying on the Beer–Lambert law to simulate X-ray images. • Supports a wide range of programming languages, including C++ and Python. • Has been benchmarked with clinically realistic phantoms. • Used in a wide range of applications in different scientific fields. • Including to produce a high number of realistic X-ray images to train ML models.

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Publication Details

Journal
Nuclear Instruments and Methods in Physics Research Section B Beam Interactions with Materials and Atoms
Published
2025-08-28
DOI
https://doi.org/10.1016/j.nimb.2025.165804
Citations
1
Primary Topic
Medical Imaging Techniques and Applications
Type
article
Field-Weighted Citation Impact
1.36
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article

X-ray simulations with gVXR in education, digital twining, experiment planning, and data analysis

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Nuclear Instruments and Methods in Physics Research Section B Beam Interactions with Materials and Atoms
Medical Imaging Techniques and Applications
1.36
article

X-ray simulations with gVXR in education, digital twining, experiment planning, and data analysis

Franck Patrick Vidal, Stefan Bosse, Claire Michelet, Sara Afshari, Radu Paul Mihail, Ali Rouwane, Jenna Tugwell-Allsup, Alberto Corbí, Cheng‐Ying Chou, Alberto Leonardi, Amin Garbout, Younes Chahid, Pierre-Frédéric Villard, Malek Racy, H. Seznec, Jean Michel Létang, Iwan T. Mitchell, R. Culver, Aarón Sújar, Éric Béchet, Alberto Albiol, Graham Kelly, Audrey Henry, Ahmed Sharif, Jean‐François Giovannelli, Bas Meere, Marcos García, Christophe Leblanc, Chris Hatton, Pascal Desbarats, Laura J. Dixon, Liam Perera, Richard C. Hanna, F. Albiol, Johan Friemann, Tristan Manchester, Simon Middleburgh, Harry Lipscomb, Martí Puig, Simon Burkhard
article en
1 citations

Abstract

gVirtualXray (gVXR) is an open-source framework that relies on the Beer–Lambert law to simulate X-ray images in real time on a graphics processor unit (GPU) using triangular meshes. A wide range of programming languages is supported (C/C++, Python, R, Ruby, Tcl, C#, Java, and GNU Octave). Simulations generated with gVXR have been benchmarked with clinically realistic phantoms (i.e. complex structures and materials) using Monte Carlo (MC) simulations, real radiographs and real digitally reconstructed radiographs (DRRs), and X-ray computed tomography (XCT). It has been used in a wide range of applications, including real-time medical simulators, proposing a new densitometric radiographic modality in clinical imaging, studying noise removal techniques in fluoroscopy, teaching particle physics and X-ray imaging to undergraduate students in engineering, and XCT to masters students, predicting image quality and artifacts in material science, etc. gVXR has also been used to produce a high number of realistic simulated images in optimisation problems and to train machine learning algorithms. This paper presents a comprehensive review of such applications of gVXR. • gVXR: open-source framework relying on the Beer–Lambert law to simulate X-ray images. • Supports a wide range of programming languages, including C++ and Python. • Has been benchmarked with clinically realistic phantoms. • Used in a wide range of applications in different scientific fields. • Including to produce a high number of realistic X-ray images to train ML models.

Nuclear Instruments and Methods in Physics Research Section B Beam Interactions with Materials and AtomsVol. 568
Centre National de la Recherche Scientifique (FR), Université de Bordeaux (FR), University of Liège (BE), Bangor University (GB), Koblenz University of Applied Sciences (DE), National Taiwan University (TW), Universidad Rey Juan Carlos (ES), University of Siegen (DE), Daresbury Laboratory (GB), Diamond Light Source (GB), Henry Royce Institute (GB), Instituto de Física Corpuscular (ES), Swiss Federal Institute of Metrology (CH), Manufacturing Technology Centre (United Kingdom) (GB), Royal Observatory (GB), Universidad Internacional De La Rioja (ES), Sciences Computers Consultants (France) (FR), Laboratoire Bordelais de Recherche en Informatique (FR), Institut Polytechnique de Bordeaux (FR), Universität Koblenz (DE), UK Astronomy Technology Centre (GB), Universitat Politècnica de València (ES), Chalmers University of Technology (SE)
Openalex Percentile: Top 16%
Medical Imaging Techniques and Applications
1.36
DOIOpen Access PDFOpenAlex
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