Astronomy:X-ray microtomography

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Short description: X-ray 3D imaging method

File:Buckelzirpe.webm File:3D rendering of a micro CT scan of a piece of dried leaf..ogv

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

Imaging system

Fan beam reconstruction

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

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

Open X-ray system

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

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

The principle

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

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

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

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

Archaeology

  • Reconstructing fire-damaged artifacts, such as the En-Gedi Scroll and Herculaneum papyri
  • Unpacking cuneiform tablets wrapped in clay envelopes[13] and clay tokens

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

  • Small electronic components. E.g. DRAM IC in plastic case.

Microdevices

Composite materials and metallic foams

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

Polymers, plastics

  • Plastic foam

Diamonds

  • Detecting defects in a diamond and finding the best way to cut it.

Food and 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

Building materials

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

  • Vertebrates
  • Invertebrates[36]

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

Space

Stereo images

  • Visualizing with blue and green or blue filters to see depth

Others

  • Cigarettes
  • Social insect nests[42]

See also

References

  1. ↑ 1.0 1.1 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. doi:10.1016/j.msea.2016.06.073. Bibcode: 2019arXiv190808757H. 
  2. ↑ X-Ray+Microtomography at the US National Library of Medicine Medical Subject Headings (MeSH)
  3. ↑ "Airway dimensions measured from micro-computed tomography and high-resolution computed tomography", Eur Respir J 28 (4): 712–720, 2006-07-26, doi:10.1183/09031936.06.00012405, PMID 16870669. 
  4. ↑ "High-resolution micro-CT for morphologic and quantitative assessment of the sinusoid in human cavernous hemangioma of the liver", PLOS One 8 (1): e53507, 2013-01-07, doi:10.1371/journal.pone.0053507, PMID 23308240, Bibcode: 2013PLoSO...853507D. 
  5. ↑ "X-ray microtomography". Journal of Microscopy 126 (2): 211–213. 1982. doi:10.1111/j.1365-2818.1982.tb00376.x. PMID 7086891. 
  6. ↑ "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. January 2016. doi:10.1016/j.nima.2015.11.007. PMID 26640309. Bibcode: 2016NIMPA.807..129G. 
  7. ↑ (in en) Industrial X-Ray Computed Tomography. Heidelberg: Springer. 2017. ISBN 978-3-319-59573-3. https://books.google.com/books?id=KLo6DwAAQBAJ&q=X-ray+microtomography. 
  8. ↑ "The ASTRA Toolbox: A platform for advanced algorithm development in electron tomography". Ultramicroscopy 157: 35–47. October 2015. doi:10.1016/j.ultramic.2015.05.002. PMID 26057688. https://ir.cwi.nl/pub/23858. 
  9. ↑ "Fast and flexible X-ray tomography using the ASTRA toolbox". Optics Express 24 (22): 25129–25147. October 2016. doi:10.1364/OE.24.025129. PMID 27828452. Bibcode: 2016OExpr..2425129V. https://ir.cwi.nl/pub/24770. 
  10. ↑ (in en) A Quasi-realtime X-ray Microtomography System at the Advanced Photon Source. United States. Department of Energy. 1999. https://books.google.com/books?id=gLfKjwEACAAJ. 
  11. ↑ Andrä, Heiko; Combaret, Nicolas; Dvorkin, Jack; Glatt, Erik; Han, Junehee; Kabel, Matthias; Keehm, Youngseuk; Krzikalla, Fabian et al. (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. doi:10.1016/j.cageo.2012.09.005. ISSN 0098-3004. Bibcode: 2013CG.....50...25A. https://www.sciencedirect.com/science/article/pii/S0098300412003147. 
  12. ↑ "Tortuosity of porous media: Image analysis and physical simulation". Earth-Science Reviews 212: 103439. January 2021. doi:10.1016/j.earscirev.2020.103439. Bibcode: 2021ESRv..21203439F. https://cronfa.swan.ac.uk/Record/cronfa55808/Download/55808__18817__4aeefe32b0ee4ae7993bff0531362902.pdf. 
  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 et al. (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. https://doi.org/10.1021/acschemneuro.9b00290. 
  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. https://pubmed.ncbi.nlm.nih.gov/23380275/. Retrieved 3 March 2021. 
  16. ↑ "Advanced Non-Destructive Ocular Visualization Methods by Improved X-Ray Imaging Techniques". PLOS ONE 12 (1): e0170633. 2017-01-27. doi:10.1371/journal.pone.0170633. PMID 28129364. Bibcode: 2017PLoSO..1270633E. 
  17. ↑ "X-ray microtomography in biology". Micron 43 (2–3): 104–15. February 2012. doi:10.1016/j.micron.2011.10.002. PMID 22036251. 
  18. ↑ "A biological screw in a beetle's leg". Science 333 (6038): 52. July 2011. doi:10.1126/science.1204245. PMID 21719669. Bibcode: 2011Sci...333...52V. 
  19. ↑ "Metamorphosis revealed: time-lapse three-dimensional imaging inside a living chrysalis". Journal of the Royal Society, Interface 10 (84): 20130304. July 2013. doi:10.1098/rsif.2013.0304. PMID 23676900. 
  20. ↑ "Development of structural colour in leaf beetles". Scientific Reports 7 (1): 1373. May 2017. doi:10.1038/s41598-017-01496-8. PMID 28465577. Bibcode: 2017NatSR...7.1373O. 
  21. ↑ "Trichobilharzia regenti (Schistosomatidae): 3D imaging techniques in characterization of larval migration through the CNS of vertebrates". Micron 83: 62–71. April 2016. 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. doi:10.1016/j.seares.2015.08.002. Bibcode: 2016JSR...113...58N. 
  23. ↑ "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. 2016-01-01. doi:10.7717/peerj.2188. PMID 27441121. 
  24. ↑ "Micro-computed tomography and brightness-mode ultrasound show air entrapments inside tablets". Current Directions in Biomedical Engineering 8 (2): 41–44. 2022. doi:10.1515/cdbme-2022-1012. 
  25. ↑ "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. February 2018. doi:10.1098/rsos.171914. PMID 29515893. Bibcode: 2018RSOS....571914N. 
  26. ↑ "Skeletal development in the African elephant and ossification timing in placental mammals". Proceedings. Biological Sciences 279 (1736): 2188–95. June 2012. doi:10.1098/rspb.2011.2481. PMID 22298853. 
  27. ↑ "Computational 3D histological phenotyping of whole zebrafish by X-ray histotomography". eLife 8. May 2019. doi:10.7554/eLife.44898. PMID 31063133. 
  28. ↑ "Prenatal cranial ossification of the humpback whale (Megaptera novaeangliae)". Journal of Morphology 276 (5): 564–82. May 2015. doi:10.1002/jmor.20367. PMID 25728778. 
  29. ↑ Gerard van Dalen, Han Blonk, Henrie van Aalst, Cris Luengo Hendriks 3-D Imaging of Foods Using X-Ray Microtomography . G.I.T. Imaging & Microscopy (March 2003), pp. 18–21
  30. ↑ "Non-destructive, high-content analysis of wheat grain traits using X-ray micro computed tomography". Plant Methods 13 (1): 76. 2017-11-01. doi:10.1186/s13007-017-0229-8. PMID 29118820. 
  31. ↑ "Micro-computed tomography shows silent bubbles in squeaky mozzarella". Current Directions in Biomedical Engineering 9 (1): 5–8. 2023. doi:10.1515/cdbme-2023-1002. 
  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" (in en). Journal of Energy Resources Technology 143 (1). doi:10.1115/1.4047589. ISSN 0195-0738. https://asmedigitalcollection.asme.org/energyresources/article/doi/10.1115/1.4047589/1084712/Upscaling-Reservoir-Rock-Porosity-by-Fractal. 
  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. doi:10.1016/j.petrol.2018.12.075. ISSN 0920-4105. Bibcode: 2019JPSE..175..654S. https://www.sciencedirect.com/science/article/pii/S0920410518311847. 
  34. ↑ Andrä, Heiko; Combaret, Nicolas; Dvorkin, Jack; Glatt, Erik; Han, Junehee; Kabel, Matthias; Keehm, Youngseuk; Krzikalla, Fabian et al. (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. doi:10.1016/j.cageo.2012.09.008. ISSN 0098-3004. Bibcode: 2013CG.....50...33A. https://www.sciencedirect.com/science/article/pii/S0098300412003172. 
  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. doi:10.1016/j.geothermics.2020.102020. ISSN 0375-6505. Bibcode: 2021Geoth..9002020C. https://www.sciencedirect.com/science/article/pii/S0375650520303126. 
  36. ↑ "High-fidelity X-ray micro-tomography reconstruction of siderite-hosted Carboniferous arachnids". Biology Letters 5 (6): 841–4. December 2009. doi:10.1098/rsbl.2009.0464. 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. https://www.wsj.com/articles/a-letter-sealed-for-centuries-has-been-readwithout-even-opening-it-11614679203. 
  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. doi:10.1038/s41467-021-21326-w. PMID 33654094. Bibcode: 2021NatCo..12.1184D. 
  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". Lunar and Planetary Science XXXIV: 1228. Bibcode: 2003LPI....34.1228J. http://www.lpi.usra.edu/meetings/lpsc2003/pdf/1228.pdf. 
  41. ↑ "Three-dimensional structure of Hayabusa samples: origin and evolution of Itokawa regolith". Science 333 (6046): 1125–8. August 2011. doi:10.1126/science.1207807. PMID 21868671. Bibcode: 2011Sci...333.1125T. 
  42. ↑ "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. January 2017. doi:10.1242/jeb.143347. PMID 28057831. 

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