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Persistent URL	http://purl.org/net/epubs/work/62591528
Record Status	Checked
Record Id	62591528
Title	X-ray simulations with gVXR in education, digital twining, experiment planning, and data analysis
Contributors	FP Vidal (STFC Daresbury Lab.), S Afshari, S Ahmed, A Albiol, F Albiol, Éric Béchet, AC Bellot, S Bosse, S Burkhard, Y Chahid (STFC UK Astronomy Technology Centre ), C Chou, R Culver, P Desbarats, L Dixon, J Friemann, A Garbout, M García-Lorenzo, J Giovannelli, R Hanna, C Hatton, A Henry, G Kelly, C Leblanc, A Leonardi, JM Létang, H Lipscomb, T Manchester, B Meere, C Michelet, S Middleburgh, RP Mihail, I Mitchell, L Perera, M Puig, M Racy, A Rouwane, H Seznec, A Sújar, J Tugwell-Allsup, P Villard
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.
Organisation	CSE-BIO , STFC , SCI-COMP
Keywords	Computed tomography , X-ray imaging, Computed tomography, Simulation, GPU programming, Digital twinning, Registration, Machine learning , Simulation , GPU programming , X-ray imaging , Registration , Digital twinning , Machine learning
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Language	English (EN)

Type	Details	URI(s)	Local file(s)	Year
Journal Article	Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 568 (2025): 165804.	doi:https://doi.o…6/j.nimb.2025.165804		2025