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 phasecomposite[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 prefixmicro- (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.
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]
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.
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]
^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. PMID26897588.
^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. PMID25728778. S2CID43353096.
^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.
