[en] 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 (CT). 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 optimization problems and to train machine learning algorithms. This paper presents applications of gVXR related to XCT.
Disciplines :
Mechanical engineering
Author, co-author :
Vidal, Franck ✱
Afshari, Shaghayegh ✱
Ahmed, Sharif ✱
Atkins, Carolyn ✱
Béchet, Eric ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Conception géométrique assistée par ordinateur
Corbi Bellot, Alberto ✱
Bosse, Stefan ✱
Chahid, Younes ✱
Chou, Cheng-Ying ✱
Culver, Robert ✱
Dixon, Lewis ✱
Friemann, Johan ✱
Garbout, Amin ✱
Hatton, Clémentine ✱
Henry, Audrey ✱
Leblanc, Christophe ✱; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Conception géométrique assistée par ordinateur
Duvauchelle, P., Freud, N., Kaftandjian, V., and Babot, D., “A computer code to simulate x-ray imaging techniques,” Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 170(1), 245–258 (2000).
Vidal, F. P., Garnier, M., Freud, N., Létang, J. M., and John, N. W., “Simulation of x-ray attenuation on the GPU,” in [Proceedings of Theory and Practice of Computer Graphics 2009], 25–32, Eurographics Association, Cardiff, UK (June 2009).
Vidal, F. P. and Villard, P.-F., “Development and validation of real-time simulation of x-ray imaging with respiratory motion,” Computerized Medical Imaging and Graphics 49, 1–15 (2016).
Freud, N., Duvauchelle, P., Létang, J. M., and Babot, D., “Fast and robust ray casting algorithms for virtual x-ray imaging,” Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 248(1), 175–180 (2006).
Villard, P.-F., Vidal, F. P., Hunt, C., Bello, F., John, N. W., Johnson, S., and Gould, D. A., “A prototype percutaneous transhepatic cholangiography training simulator with real-time breathing motion,” International Journal of Computer Assisted Radiology and Surgery 4(6), 571–578 (2009).
Vidal, F. P., Garnier, M., Freud, N., Létang, J. M., and John, N. W., “Accelerated deterministic simulation of x-ray attenuation using graphics hardware,” in [Eurographics 2010 - Poster], Poster 5011, Eurographics Association, Norrköping, Sweden (May 2010).
Vidal, F. P., Mitchell, I. T., and Létang, J. M., “Use of fast realistic simulations on GPU to extract CAD models from microtomographic data in the presence of strong CT artefacts,” Precision Engineering 74, 110–125 (2022).
Pointon, J. L., Wen, T., Tugwell-Allsup, J., Sújar, A., Létang, J. M., and Vidal, F. P., “Simulation of x-ray projections on gpu: Benchmarking gvirtualxray with clinically realistic phantoms,” Computer Methods and Programs in Biomedicine 234, 107500 (2023).
Corbi, A., Burgos, D., Vidal, F., Albiol, F., and Albiol, A., “X-ray imaging virtual online laboratory for engineering undergraduates,” European Journal of Physics 41, 014001 (Dec. 2019).
Schoonjans, T., Brunetti, A., Golosio, B., Sanchez del Rio, M., Solé, V. A., Ferrero, C., and Vincze, L., “The xraylib library for x-ray–matter interactions. recent developments,” Spectrochimica Acta Part B: Atomic Spectroscopy 66(11), 776–784 (2011).
Poludniowski, G., Omar, A., Bujila, R., and Andreo, P., “Technical note: SpekPy v2.0—a software toolkit for modeling x-ray tube spectra,” Medical Physics 48(7), 3630–3637 (2021).
Hernández, G. and Fernández, F., “A model of tungsten anode x-ray spectra,” Medical Physics 43(8Part1), 4655–4664 (2016).
Jørgensen, J. S., Ametova, E., Burca, G., Fardell, G., Papoutsellis, E., Pasca, E., Thielemans, K., Turner, M., Warr, R., Lionheart, W. R. B., and Withers, P. J., “Core Imaging Library - Part I: a versatile Python framework for tomographic imaging,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379(2204), 20200192 (2021).
Papadimitroulas, P., Erwin, W. D., Iliadou, V., Kostou, T., Loudos, G., and Kagadis, G. C., “A personalized, monte carlo-based method for internal dosimetric evaluation of radiopharmaceuticals in children,” Medical Physics 45(8), 3939–3949 (2018).
Rodriguez Perez, S., Marshall, N., Struelens, L., and Bosmans, H., “Characterization and validation of the thorax phantom lungman for dose assessment in chest radiography optimization studies,” Journal of Medical Imaging 5, 1 (02 2018).
McCormick, M., Liu, X., Ibanez, L., Jomier, J., and Marion, C., “ITK: enabling reproducible research and open science,” Frontiers in Neuroinformatics 8, 13 (2014).
Schroeder, W., Martin, K., and Lorensen, B., [The Visualization Toolkit – An Object-Oriented Approach To 3D Graphics], Kitware, Inc., fourth ed. (2006).
Vidal, F. P. and Tugwell-Allsup, J., “CT scans, 3D segmentations, digital radiograph and 3D surfaces of the Lungman phantom.” https://doi.org/10.5281/zenodo.10782644 (Mar. 2024). [Accessed 19-07-2024].
Schneider, W., Bortfeld, T., and Schlegel, W., “Correlation between CT numbers and tissue parameters needed for Monte Carlo simulations of clinical dose distributions,” Physics in Medicine & Biology 45, 459–478 (Jan. 2000).
Maire, E., Bonnard, G., Adrien, J., Boulnat, X., Létang, J. M., and Lachambre, J., “Dual beam microfocus high-energy tomography: Towards multimodal and faster laboratory experiments,” Tomography of Materials and Structures 5, 100030 (June 2024).
Létang, J. M., Lachambre, J., and Maire, E., “Cross-detector scatter issues in dual synchronous tomography: an affine projection correction protocol,” Tomography of Materials and Structures 6, 100039 (Sept. 2024).
Reinhard, C., Drakopoulos, M., Ahmed, S. I., Deyhle, H., James, A., Charlesworth, C. M., Burt, M., Sutter, J., Alexander, S., Garland, P., Yates, T., Marshall, R., Kemp, B., Warrick, E., Pueyos, A., Bradnick, B., Nagni, M., Winter, A. D., Filik, J., Basham, M., Wadeson, N., King, O. N. F., Aslani, N., and Dent, A. J., “Beamline K11 DIAD: a new instrument for dual imaging and diffraction at Diamond Light Source,” Journal of Synchrotron Radiation 28, 1985–1995 (Nov 2021).
Rit, S., Oliva, M. V., Brousmiche, S., Labarbe, R., Sarrut, D., and Sharp, G. C., “The reconstruction toolkit (rtk), an open-source cone-beam ct reconstruction toolkit based on the insight toolkit (itk),” Journal of Physics: Conference Series 489, 012079 (mar 2014).
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., Van Der Laak, J. A., Van Ginneken, B., and Sánchez, C. I., “A survey on deep learning in medical image analysis,” Medical Image Analysis 42, 60–88 (Dec. 2017).
Garcea, F., Serra, A., Lamberti, F., and Morra, L., “Data augmentation for medical imaging: A systematic literature review,” Computers in Biology and Medicine 152, 106391 (Jan. 2023).
Osuala, R., Kushibar, K., Garrucho, L., Linardos, A., Szafranowska, Z., Klein, S., Glocker, B., Diaz, O., and Lekadir, K., “Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging,” Medical Image Analysis 84, 102704 (Feb. 2023). arXiv:2107.09543 [cs, eess].
Friemann, J., Mikkelsen, L. P., Oddy, C., and Fagerström, M., “Automated generation of labeled synthetic training data for machine learning based segmentation of 3D-woven composites,” in [Proceedings of the 21st European Conference on Composite Materials: Special sessions], 8, 333–338 (2024).
Brown, L. and Long, A., “8 - modeling the geometry of textile reinforcements for composites: Texgen,” in [Composite Reinforcements for Optimum Performance (Second Edition)], Boisse, P., ed., Woodhead Publishing Series in Composites Science and Engineering, 237–265, Woodhead Publishing, second edition ed. (2021).
Bosse, S., Lehmhus, D., and Kumar, S., “Automated porosity characterization for aluminum die casting materials using x-ray radiography, synthetic x-ray data augmentation by simulation, and machine learning,” Sensors 24(9), 2933 (2024).
Shah, C., Bosse, S., and von Hehl, A., “Taxonomy of damage patterns in composite materials, measuring signals, and methods for automated damage diagnostics,” Materials 15(13), 4645 (2022).
Uhlman, N., Salamon, M., and Burtzlaff, S., “Components and Methods for Highest Resolution Computed Tomography,” International Symposium on NDT in Aerospace (Fürth, Germany) 1, 3–9 (2008).
Kerckhofs, G., Pyka, G., Moesen, M., Van Bael, S., Schrooten, J., and Wevers, M., “High-Resolution Microfocus X-Ray Computed Tomography for 3D Surface Roughness Measurements of Additive Manufactured Porous Materials,” Advanced Engineering Materials 15, 153–158 (mar 2013).
Nikishkov, Y., Seon, G., and Makeev, A., “Structural analysis of composites with porosity defects based on X-ray computed tomography,” Journal of Composite Materials 48(17), 2131–2144 (2014).
Makarkin, M. and Bratashov, D., “State-of-the-art approaches for image deconvolution problems, including modern deep learning architectures,” Micromachines 12(12) (2021).
Zhu, J., Li, K., and Hao, B., “Hybrid variational model based on alternating direction method for image restoration,” Advances in Difference Equations 2019(1) (2019).
Kaftandjian, V., Zhu, Y., Roziere, G., Peix, G., and Babot, D., “A Comparison of the Ball, Wire, Edge, and Bar/Space Pattern Techniques for Modulation Transfer Function Measurements of Linear X-Ray Detectors,” Journal of X-Ray Science and Technology 6(2), 205–221 (1996).
Lee, C., dong Song, H., and Baek, J., “3D MTF estimation using sphere phantoms for cone-beam computed tomography systems,” Medical Physics 47(7), 2838–2851 (2020).
Probst, G. M., Hou, Q., Boeckmans, B., Xiao, Y. S., and Dewulf, W., “Characterization and stability monitoring of X-ray focal spots,” CIRP Annals 69(1), 453–456 (2020).
Richardson, W. H., “Bayesian-based iterative method of image restoration∗,” J. Opt. Soc. Am. 62, 55–59 (Jan 1972).
Lucy, L. B., “An iterative technique for the rectification of observed distributions,” The Astronomical Journal 79, 745 (jun 1974).
Shaked, E., Dolui, S., and Michailovich, O. V., “Regularized richardson-lucy algorithm for reconstruction of poissonian medical images,” in [2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro], 1754–1757, IEEE (2011).
Engelhardt, M. and Baumann, J., “Determination of Size and Intensity Distribution of the Focal Spot of a Microfocus X-ray Tube Using Image Processing,” Ecndt Th.2.5.4, 1–13 (2006).
Nelder, J. A. and Mead, R., “A Simplex Method for Function Minimization,” The Computer Journal 7, 308–313 (01 1965).
Wang, Y., Yang, J., Yin, W., and Zhang, Y., “A new alternating minimization algorithm for total variation image reconstruction,” SIAM Journal on Imaging Sciences 1(3), 248–272 (2008).
Westsik, M., Wells, J. T., Chahid, Y., Morris, K., Milanova, M., Beardsley, M., Harris, M., Ward, L., Alcock, S. G., Nistea, I.-T., Cottarelli, S., Tammas-Williams, S., and Atkins, C., “From design to evaluation of an additively manufactured, lightweight, deployable mirror for Earth observation,” in [Astronomical Optics: Design, Manufacture, and Test of Space and Ground Systems IV], Hull, T. B., Kim, D., and Hallibert, P., eds., 12677, 1267704, International Society for Optics and Photonics, SPIE (2023).
Brierley, N., Nye, B., and McGuinness, J., “Mapping the spatial performance variability of an x-ray computed tomography inspection,” NDT & E International 107, 102127 (2019).
