Metal artifact reduction in micro-CT of highly attenuating objects

In many cases, samples investigated for NDT and material scientific purposes include strongly attenu-ating objects like metals or other highly dense structures. Prominent streaks or cupping decrease the accuracy of quantitative imaging and complicate image analysis, for instance when performing feature segmentation [Prevrhal2004, Sriwayu2015, Jovanovic2013] and correction of such artifacts still widely relies on empirical methods. The simulations presented in chapter 6 show that the projection-based MLE of spectral basis materials using the PLB forward-model can handle beam-hardening effects in a wide range of line-integrals. To demonstrate this ability, a CT scan of a9V block battery was performed and decomposed using the acquisition settings in table 7.7.

FOV SID SOD voxel size projections per360°

3cm 120cm 24cm 40µm 1400

Peak voltage Filtration mAs THLL THLH

140kVp 0.2mm Cu 1800 23keV 55keV

Table 7.7:CT geometry and image parameters for the battery.The voxel size and field-of-view are measured at the center of rotation. The tube loading was calculated for the total acquisition time of the measurement.

In figure 7.8 the images obtained after a conventional polychromatic reconstruction are compared to a

7 Practical implementation of photon-counting based material decomposition

Figure 7.8:Metal artifact reduction in NDT applications using PCDs.Prominent beam-hardening artifacts like cupping and streaking are drastically reduced in virtual monochromatic images generated from the spectral basis images. This is particularly beneficial in NDT applications which often involve the investigation of samples containing metal parts. ROIs used to assess the CNR are marked by the circular and square indicators.

VMI at80keV generated from the photoelectric / Compton basis images. The VMI is calculated from the decomposition data by inserting the basis material reconstructions together with the desired energy into equation 2.15. To reduce the effects of propagation of anti-correlated basis-material noise into the VMI, the photoelectric / Compton images have been postprocessed using the inhouse-developed algorithm presented in [Mechlem2016]. Thereby, noise correlations are effectively reduced. The energy level for VMI in this example was chosen such that the observed artifacts were suppressed.

Despite the high beam energy and copper filtration, significant beam-hardening artifacts are observed in the conventional image. The arrows in the polychromatic reconstruction mark positions of beam-hardening streaks that can be attributed mostly to metal artifacts. Additionally, cupping is observed towards the center of the battery. In many cases these artifacts obstruct low-contrast details in the sample structure. Due to the suitable choice of the VMI energy level, the quantitative values of the obtained voxel attenuation is comparable to the polychromatic case. However, beam-hardening associated artifacts are strongly suppressed by utilizing the spectral data. As indicated by the arrows, various features of the

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7.5 Application of PCD-based spectral imaging for material-scientific applications

Figure 7.9:CNR enhancement in the battery sample VMI.Dependent on the VMI energy the CNR was evaluated for the ROIs shown in figure 7.8. Compared to the conventional image CNR was enhanced by up to62% in postprocessed VMI images. Especially the behavior of the noise where the dependency on the energy is very weak towards larger energies is a unique feature due to the projection-based material decomposition and reduction of anti-correlation.

object could be retrieved in the spectral reconstruction.

In addition, the CNR was evaluated between the marked ROIs. Values for the CNR have been calculated for different VMI energy levels between30−150keV and for the conventional reference image. Besides the evaluation of post-processed spectral data, the CNR was also determined in VMIs created from the raw spectral basis images. The left panel in figure 7.9 shows the obtained curves dependent on the energy.

Over a wide range of energies, both CNR curves exceed the reference value set by the conventional polychromatic image. In case of de-noised basis materials the CNR was enhanced by62% while using unprocessed VMIs still yields an improvement of35% compared to the reference value. However, the values from the raw VMIs show a stronger energy-dependence towards higher energies and the image quality eventually drops below the reference. Reducing the anti-correlated noise in the basis material images leads to a much flatter curve for image noise as seen in the right panel, allowing the selection of a higher energy level which reduces beam-hardening associated artifacts more effectively. These results can be compared to the results presented in section 4.4 for the CNR and noise in a commercial dual-layer CT scanner. Especially the behavior of the noise found there is a unique feature due to the projection-based material decomposition and reduction of anti-correlation. In the previous literature on virtual monochromatic imaging, noise was reported to have a strongly pronounced global minimum and

7 Practical implementation of photon-counting based material decomposition

to increase for lower and higher energies [Yu2011, Pomerantz2013]. Furthermore, noise in VMIs was often found to be higher than in conventional images [Yu2011, Yamada2014]. Besides the discussed reduction of artifacts the results presented in this section, using our approach of combining PLB-based material decomposition with joint spectral de-noising exploiting anti-correlation, show strongly reduced noise compared to conventional images and a nearly constant noise level at higher energies.

Although the data presented in this section only shows a single application, the general ability of projection-based material decomposition to handle and correct beam-hardening associated artifacts in micro-CT is clearly demonstrated and encourages further refinement and application of this method.