Artefacts

Artefacts are common in µCT, and they are detrimental to the quality of reconstructed data which impacts quantification values. There are many sources of artefacts arising from µCT, from sample movement to general noise, and so the causes of the artefacts must be known to suppress or remove them from the image. The artefacts described below are the most relevant to the work ahead.

2.3.2.1 Aliasing artefacts

As the reconstruction algorithm is an approximation of the object being scanned, the quality of the final reconstruction relies on the number of projections taken and the samples per projection (i.e. the number of pixels across the CCD). Having too few projections will lead to the reconstruction having streaky lines as there is insufficient information, whilst having too few samples per projection will lead to blurry images. The rule of thumb is the number of samples per projection should be at least equal to the number of projections taken, although ^._/∙ 1 is recommended where N is the number of projections taken (Stock, 1999).

2.3.2.2 Beam hardening

For polychromatic sources, the emitted X0ray beam is a spectrum of different energies, the shape of which typically depends on the voltage across the cathode and anode and the anode material used (in this case tungsten). As the X0ray beam passes through a material, the lower energy photons are absorbed more readily, leaving the higher energy X0rays to pass through the remainder of the material. As the energy profile of the beam changes, the apparent X0ray absorption changes leading to an image that is brighter on the outside and darker on the inside, known as ‘cupping’ (Stock, 1999).

The occurrence of beam hardening means that accurate quantification of the local densities along an X0ray path is extremely difficult.

To correct for this artefact, filters can be used that remove the low energy X0rays at the source or by measuring a wedge phantom that approximates the amount of beam hardening in a scan. Filters are typically made of copper or steel. The use of a monochromatic X0ray source (e.g. as in many synchrotron sources) or dual energy techniques can also correct beam hardening artefacts post0 scanning, but lengthens scan times.

2.3.2.3 Metal artefacts

For an X0ray beam to penetrate a thick, dense material, the X0ray energy must be sufficiently high.

When the linear attenuation coefficient is so high that the X0ray signal that reaches the detector is very low, this will cause a bright halo or streaky lines leaching into the pixels surrounding the dense object in the reconstruction (Stauber and Muller, 2008). Metals most frequently exhibit this problem.

When imaging metal implants with bone and soft tissues, a compromise in X0ray energy is made as too high energies will create poor contrast in the bone and soft tissue phases, whilst too low energies can exacerbate the metal artefacts. In the past, the presence of beam hardening artefacts severely limited the X0ray imaging of metals, but improved algorithms and the use of 2 mm aluminium or 10 mm polymethylmethacrylate filters have reduced these effects (Jaecques et al., 2004; Koseki et al., 2008; Tuy, 1993; Wang et al., 1996; Wei et al., 2004). When metals are fully opaque to X0rays, this causes the projections to show no information in the area behind the surface. In these cases, Lewitt and Bates (Lewitt and Bates, 1978) developed an algorithm that interpolated the missing data in the projections, thus improving the overall reconstruction and this algorithm was successfully implemented in the study of in vivo hip implants (Hinderling et al., 1979). An improvement on the polynomial interpolation algorithm, was a smoothing0scaling method that was developed due to the difficulties in interpolating the bone profile in scans of in vivo metallic implants (Wei et al., 2004).

The bone is first thresholded and smoothed such that the bone has similar intensity to soft tissue. The metal is also thresholded and its image profile is corrected by polynomial interpolation. The two sets of images are used to create new projections from which the corrected image is reconstructed. A similar method proposed by Koseki et al. (Koseki et al., 2008) used a simple thresholding of the

metallic areas from an initial scan, to refine the projections of the next scan by subtracting the artefacts from the reconstruction. Although this method is seen to be very successful in simple or repeating structures, there were limitations in its ability to reduce metal artefacts in complex or overlapping structures, making these projections0based algorithms less effective in complex porous Ti structures where the relative pore space is limited. Both methods require additional scanning times in order to obtain information of the metallic structure prior to correction. A comparison of the two techniques is shown in Figure 2.8.

Wang et al. (Wang et al., 1996) adapted an ‘expectation maximisation’ (EM) formula to produce an iterative reconstruction technique as opposed to filtered backprojection. The EM algorithm is an algorithm applied to statistical models to reach an optimum estimate of the parameters involved by repeating the expectation and maximisation steps. This approach is extremely computationally expensive.

Figure 2.8 (a) Shows two metal prostheses inside a patient before metal artefact correction, (b) shows the prostheses after being corrected by the polynomial interpolation by Wei et al. The next two images shows the reconstructed data of a Ti structure before (c) and after (d) correction by the algorithm proposed by Koseki et al.

Images (a) and (b) were adapted from (Wei et al., 2004); (c) and (d) from (Koseki et al., 2008)

Despite both methods being very promising, metal artefacts were not corrected at the projection stage as the software for these was not available in the equipment used for this work. In order to minimise metal artefacts, a 2 mm aluminium filter was placed in front of the X0ray source in order to absorb low energy X0rays. Also, a post0reconstruction algorithm was developed to accommodate for metal artefacts in quantification – see section 3.2 for more information.

2.3.2.4 Ring artefacts

Ring artefacts can occur due to dust interfering with the detector or dead pixels. These cause a blank pixel in the projections which manifest as rings on the reconstructed volume. These are easily removed by removing dead pixels from the reconstruction by appropriate bright0field calibration images or post0reconstruction image analysis (Rashid et al., 2012).