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X-ray microtomography

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3D rendering of a micro CT of a treehopper.
3D rendering of a μCT scan of a leaf piece, resolution circa 40 μm/voxel.
Two phase μCT analysis of Ti2AlC/Al MAX phase composite[1]

In radiography, X-ray microtomography uses X-rays to create cross-sections of a physical object that can be used to recreate a virtual model (3D model) without destroying the original object. It is similar to tomography and X-ray computed tomography. The prefix micro- (symbol: μ) is used to indicate that the pixel sizes of the cross-sections are in the micrometre range.[2] These pixel sizes have also resulted in creation of its synonyms high-resolution X-ray tomography, micro-computed tomography (micro-CT or μCT), and similar terms. Sometimes the terms high-resolution computed tomography (HRCT) and micro-CT are differentiated,[3] but in other cases the term high-resolution micro-CT is used.[4] Virtually all tomography today is computed tomography.

Micro-CT has applications both in medical imaging and in industrial computed tomography. In general, there are two types of scanner setups. In one setup, the X-ray source and detector are typically stationary during the scan while the sample/animal rotates. The second setup, much more like a clinical CT scanner, is gantry based where the animal/specimen is stationary in space while the X-ray tube and detector rotate around. These scanners are typically used for small animals (in vivo scanners), biomedical samples, foods, microfossils, and other studies for which minute detail is desired.

The first X-ray microtomography system was conceived and built by Jim Elliott in the early 1980s. The first published X-ray microtomographic images were reconstructed slices of a small tropical snail, with pixel size about 50 micrometers.[5]

Working principle

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Imaging system

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Fan beam reconstruction

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The fan-beam system is based on a one-dimensional (1D) X-ray detector and an electronic X-ray source, creating 2D cross-sections of the object. Typically used in human computed tomography systems.

Cone beam reconstruction

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See also: Cone beam reconstruction

The cone-beam system is based on a 2D X-ray detector (camera) and an electronic X-ray source, creating projection images that later will be used to reconstruct the image cross-sections.

Open/Closed systems

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Open X-ray system

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In an open system, X-rays may escape or leak out, thus the operator must stay behind a shield, have special protective clothing, or operate the scanner from a distance or a different room. Typical examples of these scanners are the human versions, or designed for big objects.

Closed X-ray system

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In a closed system, X-ray shielding is put around the scanner so the operator can put the scanner on a desk or special table. Although the scanner is shielded, care must be taken and the operator usually carries a dosimeter, since X-rays have a tendency to be absorbed by metal and then re-emitted like an antenna. Although a typical scanner will produce a relatively harmless volume of X-rays, repeated scannings in a short timeframe could pose a danger. Digital detectors with small pixel pitches and micro-focus x-ray tubes are usually employed to yield in high resolution images.[6]

Closed systems tend to become very heavy because lead is used to shield the X-rays. Therefore, the smaller scanners only have a small space for samples.

3D image reconstruction

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The principle

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Because microtomography scanners offer isotropic, or near isotropic, resolution, display of images does not need to be restricted to the conventional axial images. Instead, it is possible for a software program to build a volume by 'stacking' the individual slices one on top of the other. The program may then display the volume in an alternative manner.[7]

Image reconstruction software

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For X-ray microtomography, powerful open source software is available, such as the ASTRA toolbox.[8][9] The ASTRA Toolbox is a MATLAB and python toolbox of high-performance GPU primitives for 2D and 3D tomography, from 2009 to 2014 developed by iMinds-Vision Lab, University of Antwerp and since 2014 jointly developed by iMinds-VisionLab, UAntwerpen and CWI, Amsterdam. The toolbox supports parallel, fan, and cone beam, with highly flexible source/detector positioning. A large number of reconstruction algorithms are available, including FBP, ART, SIRT, SART, CGLS.[10]

For 3D visualization, tomviz is a popular open-source tool for tomography.[citation needed]

Volume rendering

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Volume rendering is a technique used to display a 2D projection of a 3D discretely sampled data set, as produced by a microtomography scanner. Usually these are acquired in a regular pattern, e.g., one slice every millimeter, and usually have a regular number of image pixels in a regular pattern. This is an example of a regular volumetric grid, with each volume element, or voxel represented by a single value that is obtained by sampling the immediate area surrounding the voxel.

Image segmentation

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Where different structures have similar threshold density, it can become impossible to separate them simply by adjusting volume rendering parameters. The solution is called segmentation, a manual or automatic procedure that can remove the unwanted structures from the image.[11][12]

Typical use

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Archaeology

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See also: Archaeology

Biomedical

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See also: Biomedical
  • Both in vitro and in vivo small animal imaging
  • Neurons[14]
  • Human skin samples
  • Bone samples, including teeth,[15] ranging in size from rodents to human biopsies
  • Lung imaging using respiratory gating
  • Cardiovascular imaging using cardiac gating
  • Imaging of the human eye, ocular microstructures and tumors[16]
  • Tumor imaging (may require contrast agents)
  • Soft tissue imaging[17]
  • Insects[18] – Insect development[19][20]
  • Parasitology – migration of parasites,[21] parasite morphology[22][23]
  • Tablet consistency checks[24]

Developmental biology

  • Tracing the development of the extinct Tasmanian tiger during growth in the pouch[25]
  • Model and non-model organisms (elephants,[26] zebrafish,[27] and whales[28])

Electronics

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See also: Electronics
  • Small electronic components. E.g. DRAM IC in plastic case.

Microdevices

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Composite materials and metallic foams

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See also: Metal foam
  • Ceramics and Ceramic–Metal composites.[1] Microstructural analysis and failure investigation
  • Composite material with glass fibers 10 to 12 micrometres in diameter

Polymers, plastics

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See also: plastics

Diamonds

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See also: Diamond
  • Detecting defects in a diamond and finding the best way to cut it.

Food and seeds

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See also: seeds
  • 3-D imaging of foods[29]
  • Analysing heat and drought stress on food crops[30]
  • Bubble detection in squeaky cheese[31]

Wood and paper

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See also: paper

Building materials

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See also: Building materials

Geology

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See also: Geology

In geology it is used to analyze micro pores in the reservoir rocks,[32][33] it can used in microfacies analysis for sequence stratigraphy. In petroleum exploration it is used to model the petroleum flow under micro pores and nano particles.

It can give a resolution up to 1 nm.

Fossils

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See also: Fossils

Microfossils

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See also: Microfossils
X-ray microtomography of a radiolarian, Triplococcus acanthicus
This is a microfossil from the Middle Ordovician with four nested spheres. The innermost sphere is highlighted red. Each segment is shown at the same scale.[37]
  • Benthonic foraminifers

Palaeography

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See also: Palaeography

Space

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See also: Space

Stereo images

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See also: stereoscopy
  • Visualizing with blue and green or blue filters to see depth

Others

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See also

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References

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  1. ^ a b Hanaor, D.A.H.; Hu, L.; Kan, W.H.; Proust, G.; Foley, M.; Karaman, I.; Radovic, M. (2019). "Compressive performance and crack propagation in Al alloy/Ti2AlC composites". Materials Science and Engineering A. 672: 247–256. arXiv:1908.08757. Bibcode:2019arXiv190808757H. doi:10.1016/j.msea.2016.06.073. S2CID 201645244.
  2. ^ X-Ray+Microtomography at the U.S. National Library of Medicine Medical Subject Headings (MeSH)
  3. ^ Dame Carroll JR, Chandra A, Jones AS, Berend N, Magnussen JS, King GG (2006-07-26), "Airway dimensions measured from micro-computed tomography and high-resolution computed tomography", Eur Respir J, 28 (4): 712–720, doi:10.1183/09031936.06.00012405, PMID 16870669.
  4. ^ Duan J, Hu C, Chen H (2013-01-07), "High-resolution micro-CT for morphologic and quantitative assessment of the sinusoid in human cavernous hemangioma of the liver", PLOS One, 8 (1): e53507, Bibcode:2013PLoSO...853507D, doi:10.1371/journal.pone.0053507, PMC 3538536, PMID 23308240.
  5. ^ Elliott JC, Dover SD (1982). "X-ray microtomography". Journal of Microscopy. 126 (2): 211–213. doi:10.1111/j.1365-2818.1982.tb00376.x. PMID 7086891. S2CID 2231984.
  6. ^ Ghani MU, Zhou Z, Ren L, Li Y, Zheng B, Yang K, Liu H (January 2016). "Investigation of spatial resolution characteristics of an in vivo micro computed tomography system". Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 807: 129–136. Bibcode:2016NIMPA.807..129G. doi:10.1016/j.nima.2015.11.007. PMC 4668590. PMID 26640309.
  7. ^ Carmignato S, Dewulf W, Leach R (2017). Industrial X-Ray Computed Tomography. Heidelberg: Springer. ISBN 978-3-319-59573-3.
  8. ^ van Aarle W, Palenstijn WJ, De Beenhouwer J, Altantzis T, Bals S, Batenburg KJ, Sijbers J (October 2015). "The ASTRA Toolbox: A platform for advanced algorithm development in electron tomography". Ultramicroscopy. 157: 35–47. doi:10.1016/j.ultramic.2015.05.002. hdl:10067/1278340151162165141. PMID 26057688.
  9. ^ van Aarle W, Palenstijn WJ, Cant J, Janssens E, Bleichrodt F, Dabravolski A, et al. (October 2016). "Fast and flexible X-ray tomography using the ASTRA toolbox". Optics Express. 24 (22): 25129–25147. Bibcode:2016OExpr..2425129V. doi:10.1364/OE.24.025129. hdl:10067/1392160151162165141. PMID 27828452.
  10. ^ A Quasi-realtime X-ray Microtomography System at the Advanced Photon Source. United States. Department of Energy. 1999.
  11. ^ Andrä, Heiko; Combaret, Nicolas; Dvorkin, Jack; Glatt, Erik; Han, Junehee; Kabel, Matthias; Keehm, Youngseuk; Krzikalla, Fabian; Lee, Minhui; Madonna, Claudio; Marsh, Mike; Mukerji, Tapan; Saenger, Erik H.; Sain, Ratnanabha; Saxena, Nishank (2013-01-01). "Digital rock physics benchmarks—Part I: Imaging and segmentation". Computers & Geosciences. Benchmark problems, datasets and methodologies for the computational geosciences. 50: 25–32. Bibcode:2013CG.....50...25A. doi:10.1016/j.cageo.2012.09.005. ISSN 0098-3004. S2CID 5722082.
  12. ^ Fu J, Thomas HR, Li C (January 2021). "Tortuosity of porous media: Image analysis and physical simulation" (PDF). Earth-Science Reviews. 212: 103439. Bibcode:2021ESRv..21203439F. doi:10.1016/j.earscirev.2020.103439. S2CID 229386129.
  13. ^ Unpacking a Cuneiform Tablet wrapped in a clay envelope on YouTube. Data processing and visualization using the GigaMesh Software Framework, cf. doi:10.11588/heidok.00026892.
  14. ^ Depannemaecker, Damien; Santos, Luiz E. Canton; de Almeida, Antonio-Carlos Guimarães; Ferreira, Gustavo B. S.; Baraldi, Giovanni L.; Miqueles, Eduardo X.; de Carvalho, Murilo; Costa, Gabriel Schubert Ruiz; Marques, Marcia J. Guimarães; Scorza, Carla A.; Rinkel, Jean (2019-08-21). "Gold Nanoparticles for X-ray Microtomography of Neurons". ACS Chemical Neuroscience. 10 (8): 3404–3408. doi:10.1021/acschemneuro.9b00290. PMID 31274276. S2CID 195805317.
  15. ^ Davis, GR; Evershed, AN; Mills, D (May 2013). "Quantitative high contrast X-ray microtomography for dental research". J. Dent. 41 (5): 475–82. doi:10.1016/j.jdent.2013.01.010. PMID 23380275. Retrieved 3 March 2021.
  16. ^ Enders C, Braig EM, Scherer K, Werner JU, Lang GK, Lang GE, et al. (2017-01-27). "Advanced Non-Destructive Ocular Visualization Methods by Improved X-Ray Imaging Techniques". PLOS ONE. 12 (1): e0170633. Bibcode:2017PLoSO..1270633E. doi:10.1371/journal.pone.0170633. PMC 5271321. PMID 28129364.
  17. ^ Mizutani R, Suzuki Y (February 2012). "X-ray microtomography in biology". Micron. 43 (2–3): 104–15. arXiv:1609.02263. doi:10.1016/j.micron.2011.10.002. PMID 22036251. S2CID 13261178.
  18. ^ van de Kamp T, Vagovič P, Baumbach T, Riedel A (July 2011). "A biological screw in a beetle's leg". Science. 333 (6038): 52. Bibcode:2011Sci...333...52V. doi:10.1126/science.1204245. PMID 21719669. S2CID 8527127.
  19. ^ Lowe T, Garwood RJ, Simonsen TJ, Bradley RS, Withers PJ (July 2013). "Metamorphosis revealed: time-lapse three-dimensional imaging inside a living chrysalis". Journal of the Royal Society, Interface. 10 (84): 20130304. doi:10.1098/rsif.2013.0304. PMC 3673169. PMID 23676900.
  20. ^ Onelli OD, Kamp TV, Skepper JN, Powell J, Rolo TD, Baumbach T, Vignolini S (May 2017). "Development of structural colour in leaf beetles". Scientific Reports. 7 (1): 1373. Bibcode:2017NatSR...7.1373O. doi:10.1038/s41598-017-01496-8. PMC 5430951. PMID 28465577.
  21. ^ Bulantová J, Macháček T, Panská L, Krejčí F, Karch J, Jährling N, et al. (April 2016). "Trichobilharzia regenti (Schistosomatidae): 3D imaging techniques in characterization of larval migration through the CNS of vertebrates". Micron. 83: 62–71. doi:10.1016/j.micron.2016.01.009. PMID 26897588.
  22. ^ Noever, Christoph; Keiler, Jonas; Glenner, Henrik (2016-07-01). "First 3D reconstruction of the rhizocephalan root system using MicroCT". Journal of Sea Research. Ecology and Evolution of Marine Parasites and Diseases. 113: 58–64. Bibcode:2016JSR...113...58N. doi:10.1016/j.seares.2015.08.002. hdl:1956/12721.
  23. ^ Nagler C, Haug JT (2016-01-01). "Functional morphology of parasitic isopods: understanding morphological adaptations of attachment and feeding structures in Nerocila as a pre-requisite for reconstructing the evolution of Cymothoidae". PeerJ. 4: e2188. doi:10.7717/peerj.2188. PMC 4941765. PMID 27441121.
  24. ^ Carlson CS, Hannula M, Postema M (2022). "Micro-computed tomography and brightness-mode ultrasound show air entrapments inside tablets". Current Directions in Biomedical Engineering. 8 (2): 41–44. doi:10.1515/cdbme-2022-1012. S2CID 251981681.
  25. ^ Newton AH, Spoutil F, Prochazka J, Black JR, Medlock K, Paddle RN, et al. (February 2018). "Letting the 'cat' out of the bag: pouch young development of the extinct Tasmanian tiger revealed by X-ray computed tomography". Royal Society Open Science. 5 (2): 171914. Bibcode:2018RSOS....571914N. doi:10.1098/rsos.171914. PMC 5830782. PMID 29515893.
  26. ^ Hautier L, Stansfield FJ, Allen WR, Asher RJ (June 2012). "Skeletal development in the African elephant and ossification timing in placental mammals". Proceedings. Biological Sciences. 279 (1736): 2188–95. doi:10.1098/rspb.2011.2481. PMC 3321712. PMID 22298853.
  27. ^ Ding Y, Vanselow DJ, Yakovlev MA, Katz SR, Lin AY, Clark DP, et al. (May 2019). "Computational 3D histological phenotyping of whole zebrafish by X-ray histotomography". eLife. 8. doi:10.7554/eLife.44898. PMC 6559789. PMID 31063133.
  28. ^ Hampe O, Franke H, Hipsley CA, Kardjilov N, Müller J (May 2015). "Prenatal cranial ossification of the humpback whale (Megaptera novaeangliae)". Journal of Morphology. 276 (5): 564–82. doi:10.1002/jmor.20367. PMID 25728778. S2CID 43353096.
  29. ^ Gerard van Dalen, Han Blonk, Henrie van Aalst, Cris Luengo Hendriks 3-D Imaging of Foods Using X-Ray Microtomography Archived July 19, 2011, at the Wayback Machine. G.I.T. Imaging & Microscopy (March 2003), pp. 18–21
  30. ^ Hughes N, Askew K, Scotson CP, Williams K, Sauze C, Corke F, et al. (2017-11-01). "Non-destructive, high-content analysis of wheat grain traits using X-ray micro computed tomography". Plant Methods. 13 (1): 76. doi:10.1186/s13007-017-0229-8. PMC 5664813. PMID 29118820.
  31. ^ Nurkkala E, Hannula M, Carlson CS, Hyttinen J, Hopia A, Postema M (2023). "Micro-computed tomography shows silent bubbles in squeaky mozzarella". Current Directions in Biomedical Engineering. 9 (1): 5–8. doi:10.1515/cdbme-2023-1002. S2CID 262087123.
  32. ^ Munawar, Muhammad Jawad; Vega, Sandra; Lin, Chengyan; Alsuwaidi, Mohammad; Ahsan, Naveed; Bhakta, Ritesh Ramesh (2021-01-01). "Upscaling Reservoir Rock Porosity by Fractal Dimension Using Three-Dimensional Micro-Computed Tomography and Two-Dimensional Scanning Electron Microscope Images". Journal of Energy Resources Technology. 143 (1). doi:10.1115/1.4047589. ISSN 0195-0738. S2CID 224851782.
  33. ^ Sun, Huafeng; Belhaj, Hadi; Tao, Guo; Vega, Sandra; Liu, Luofu (2019-04-01). "Rock properties evaluation for carbonate reservoir characterization with multi-scale digital rock images". Journal of Petroleum Science and Engineering. 175: 654–664. Bibcode:2019JPSE..175..654S. doi:10.1016/j.petrol.2018.12.075. ISSN 0920-4105. S2CID 104311947.
  34. ^ Andrä, Heiko; Combaret, Nicolas; Dvorkin, Jack; Glatt, Erik; Han, Junehee; Kabel, Matthias; Keehm, Youngseuk; Krzikalla, Fabian; Lee, Minhui; Madonna, Claudio; Marsh, Mike; Mukerji, Tapan; Saenger, Erik H.; Sain, Ratnanabha; Saxena, Nishank (2013-01-01). "Digital rock physics benchmarks—part II: Computing effective properties". Computers & Geosciences. Benchmark problems, datasets and methodologies for the computational geosciences. 50: 33–43. Bibcode:2013CG.....50...33A. doi:10.1016/j.cageo.2012.09.008. ISSN 0098-3004.
  35. ^ Cid, Héctor Eduardo; Carrasco-Núñez, Gerardo; Manea, Vlad Constantin; Vega, Sandra; Castaño, Victor (2021-02-01). "The role of microporosity on the permeability of volcanic-hosted geothermal reservoirs: A case study from Los Humeros, Mexico". Geothermics. 90: 102020. Bibcode:2021Geoth..9002020C. doi:10.1016/j.geothermics.2020.102020. ISSN 0375-6505. S2CID 230555156.
  36. ^ Garwood R, Dunlop JA, Sutton MD (December 2009). "High-fidelity X-ray micro-tomography reconstruction of siderite-hosted Carboniferous arachnids". Biology Letters. 5 (6): 841–4. doi:10.1098/rsbl.2009.0464. PMC 2828000. PMID 19656861.
  37. ^ Kachovich, S., Sheng, J. and Aitchison, J.C., 2019. Adding a new dimension to investigations of early radiolarian evolution. Scientific reports, 9(1), pp.1-10. doi:10.1038/s41598-019-42771-0.
  38. ^ Castellanos, Sara (2 March 2021). "A Letter Sealed for Centuries Has Been Read—Without Even Opening It". The Wall Street Journal. Retrieved 2 March 2021.
  39. ^ Dambrogio, Jana; Ghassaei, Amanda; Staraza Smith, Daniel; Jackson, Holly; Demaine, Martin L. (2 March 2021). "Unlocking history through automated virtual unfolding of sealed documents imaged by X-ray microtomography". Nature Communications. 12 (1): 1184. Bibcode:2021NatCo..12.1184D. doi:10.1038/s41467-021-21326-w. PMC 7925573. PMID 33654094.
  40. ^ Jurewicz, A. J. G.; Jones, S. M.; Tsapin, A.; Mih, D. T.; Connolly, H. C. Jr.; Graham, G. A. (2003). "Locating Stardust-like Particles in Aerogel Using X-Ray Techniques" (PDF). Lunar and Planetary Science. XXXIV: 1228. Bibcode:2003LPI....34.1228J.
  41. ^ Tsuchiyama A, Uesugi M, Matsushima T, Michikami T, Kadono T, Nakamura T, et al. (August 2011). "Three-dimensional structure of Hayabusa samples: origin and evolution of Itokawa regolith". Science. 333 (6046): 1125–8. Bibcode:2011Sci...333.1125T. doi:10.1126/science.1207807. PMID 21868671. S2CID 206534927.
  42. ^ Perna A, Theraulaz G (January 2017). "When social behaviour is moulded in clay: on growth and form of social insect nests". The Journal of Experimental Biology. 220 (Pt 1): 83–91. doi:10.1242/jeb.143347. PMID 28057831.
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