Comparison of ring artifact removal methods using flat panel detector based CT images

Abstract

Background: Ring artifacts are the concentric rings superimposed on the tomographic images often caused by the defective and insufficient calibrated detector elements as well as by the damaged scintillator crystals of the flat panel detector. It may be also generated by objects attenuating X-rays very differently in different projection direction. Ring artifact reduction techniques so far reported in the literature can be broadly classified into two groups. One category of the approaches is based on the sinogram processing also known as the pre-processing techniques and the other category of techniques perform processing on the 2-D reconstructed images, recognized as the post-processing techniques in the literature. The strength and weakness of these categories of approaches are yet to be explored from a common platform.

Method: In this paper, a comparative study of the two categories of ring artifact reduction techniques basically designed for the multi-slice CT instruments is presented from a common platform. For comparison, two representative algorithms from each of the two categories are selected from the published literature. A very recently reported state-of-the-art sinogram domain ring artifact correction method that classifies the ring artifacts according to their strength and then corrects the artifacts using class adaptive correction schemes is also included in this comparative study. The first sinogram domain correction method uses a wavelet based technique to detect the corrupted pixels and then using a simple linear interpolation technique estimates the responses of the bad pixels. The second sinogram based correction method performs all the filtering operations in the transform domain, i.e., in the wavelet and Fourier domain. On the other hand, the two post-processing based correction techniques actually operate on the polar transform domain of the reconstructed CT images. The first method extracts the ring artifact template vector using a homogeneity test and then corrects the CT images by subtracting the artifact template vector from the uncorrected images. The second post-processing based correction technique performs median and mean filtering on the reconstructed images to produce the corrected images.

Results: The performances of the comparing algorithms have been tested by using both quantitative and perceptual measures. For quantitative analysis, two different numerical performance indices are chosen. On the other hand, different types of artifact patterns, e.g., single/band ring, artifacts from defective and mis-calibrated detector elements, rings in highly structural object and also in hard object, rings from different flat-panel detectors are analyzed to perceptually investigate the strength and weakness of the five methods. An investigation has been also carried out to compare the efficacy of these algorithms in correcting the volume images from a cone beam CT with the parameters determined from one particular slice. Finally, the capability of each correction technique in retaining the image information (e.g., small object at the iso-center) accurately in the corrected CT image has been also tested.

Conclusions: The results show that the performances of the algorithms are limited and none is fully suitable for correcting different types of ring artifacts without introducing processing distortion to the image structure. To achieve the diagnostic quality of the corrected slices a combination of the two approaches (sinogram- and post-processing) can be used. Also the comparing methods are not suitable for correcting the volume images from a cone beam flat-panel detector based CT.

Figure 1

The correction process shown for the modified wavelet method. (a) Sinogram image of an electrolytic capacitor, (b-e) subset sinograms, P_k(n, j) for k = 1 to 4, respectively, (f-g) image view of D_k(n, j) and T_k(n, j) for k = 4, respectively, (h) variation of y_k(j) with j. A threshold T_HN should be properly selected so that only the bad pixels are detected and the edges remain outside of detection.

Figure 2

Drawbacks of the wavelet-Fourier method. (a-c) Magnified image view of the coefficients of the horizontal, diagonal and filtered (Fourier) vertical detail bands, respectively. These three bands' coefficients must be free from stripe information. But we see that vertical stripes are present in these three detail band coefficients as marked by arrows.

Figure 3

Removal of ring artifacts from an electrolytic capacitor image. (a) Uncorrected, original image. (b-f) Corrected images by applying the modified wavelet method without normalization (L_max = 3, k₀ = 5.85 and T_HN = 0.33), WF (L = 3, wname='db43' and σ = 7.5), RCHT (W = 125 and T = 0.0023), RCP and MWPN (L_max = 2, k₀ = 5.85 and T_HN = 0.33, N = 4) methods, respectively. Without using the normalization technique, the modified wavelet method alone is not appropriate to delete the weak rings (such a ring is shown in (b) as marked by an arrow). Wavelet-Fourier method blurs the image in different regions as marked by boxes. (g-i) Zoomed view of the ROIs (b), (e) and (f), respectively. The effectiveness of the normalization technique in removing the weak rings is evident in (f). (j-l) Effect of the pre-correction using [17]. Corrected ROIs are shown in (j-l) by the WF (L = 3, wname='db43' and σ = 7.5), RCHT (W = 125 and T = 0.0023) and RCP methods (applied on the pre-corrected image), respectively. It is illustrated that as the varying intensity rings are pre-corrected, therefore, the remaining mis-calibration rings are suppressed by these three methods. But blurring is observed at the ring location marked by dashed boxes due to the longer filter lengths in these three methods. Same window settings 'C/W = 0.5880/0.1686' are used for all sub-figures.

Figure 4

Removal of ring artifacts from the micro-CT and dental-CT images of an animal bone. (a) Uncorrected bone image from micro-CT machine, (b-e) corrected images by using the MWPN (L_max = 1, k₀ = 5.10, T_HN = 0.10 and N = 2), WF (L = 4, wname='db42' and σ = 10.0), RCHT (W = 125 and T = 0.0025), and RCP methods, respectively. (f-h) Effect of pre-correction on the WF, RCHT and RCP methods. Corrected images by using the WF (L = 2, wname='db41' and σ = 0.5), RCHT (W = 125 and T = 0.0005), and RCP methods applying on the pre-corrected image are displayed in (f-h), respectively. (i) Uncorrected bone image from dental-CT machine, (j-m) corrected images by using the MWPN (L_max = 5, k₀ = 5.10, T_HN = 0.10 and N = 8), WF (L = 5, wname='db42' and σ = 10.5), RCHT (W = 125 and T = 0.0023), and RCP methods, respectively. (n-p) Applying correction on the pre-corrected image by using the WF (L = 5, wname='db42' and σ = 3.5), RCHT (W = 125 and T = 0.0019), and RCP methods, respectively. The window settings for (a-h) are 'C/W = 0.4132/0.2994' and that for (i-p) are 'C/W = 0.4714/0.1998'.

Figure 5

Removal of ring artifacts from three 2-D slices of the reconstructed rat abdomen image of a 3-D CBVCT image. (a-c) Uncorrected images, corrected images by using the (d-f) MWPN method (L_max = 1, k₀ = 6.4, T_HN = 0.22 and N = 3), (g-i) WF method (L = 4, wname='db42' and σ = 4.5), (j-l) RCHT method (W = 125 and T = 0.0025) and (m-o) RCP method. Same window settings 'C/W = 0.2628/0.0481' are used for all sub-figures.

Figure 6

Removal of ring artifacts from a rabbit bone with a metal implant image. (a) Uncorrected, initial image, (b) magnified view of the ROI in (a). (c-f) Corrected ROIs by using the MWPN (L_max = 1, k₀ = 8.1, T_HN = 0.25 and N = 8), WF (L = 4, wname='db42' and σ = 8.0), RCHT (W = 125 and T = 0.0019), and RCP methods, respectively. (g-j) Effect of pre-correction on the MWPN, WF, RCHT and RCP methods. Corrected images by using the WF (L = 4, wname='db42' and σ = 4.5), RCHT (W = 125 and T = 0.0021), and RCP methods applying on the pre-corrected image are displayed in (g-j), respectively. (k-n) Difference ROIs between the uncorrected and corrected ROIs (g-j), respectively. Same window settings 'C/W = 0.6716/0.5827' are used for all sub-figures.

Figure 7

Removal of ring artifacts from a uniform phantom image with a gold wire located at the iso-center. (a) Uncorrected, initial projection image of a gold wire placed at near of the iso-center of a uniform phantom, (b) zoomed view of the ROI selected in (a). (c) Modified ROI. (d) The reconstructed uniform phantom image with a gold wire located at the iso-center, (e) magnified view of the ROI in (d). (f-i) Corrected ROIs by using the MWPN (L_max = 1, k₀ = 8.3, T_HN = 0.20 and N = 4), WF (L = 4, wname='db41' and σ = 6.0), RCHT (W = 125 and T = 0.0019), and RCP methods, respectively. Same window settings 'C/W = 0.5/1.0' are used for (d-i).

Figure 8

Removal of ring artifacts from the analyzed CT images by applying the SBRC method and effect of using wavelet-analysis-based method on cone beam CT volume images. (a) Corrected bone image obtained from micro-CT machine (r_max = 15, r_min = 1.5 and l_m = 5) (C/W = 0.4132/0.2994), (b) corrected bone image from dental-CT machine (r_max = 15, r_min = 1.5 and l_m = 5) (C/W = 0.4714/0.1998), (c-e) corrected three rat abdomen slices (r_max = 15, r_min = 1.5 and l_m = 5) (C/W = 0.2628/0.0481), (f) corrected ROI of the rabbit image (r_max = 15, r_min = 1.5 and l_m = 5) (C/W = 0.6716/0.5827), (g) difference image between the uncorrected and corrected ROI of rabbit (C/W = 0.6716/0.5827), (h) corrected uniform phantom image with a gold wire located at the iso-center (r_max = 15, r_min = 1.5 and l_m = 5) (C/W = 0.5/1.0). (i) Corrected first rat abdomen slice using the wavelet-analysis-based method [13] (C/W = 0.2628/0.0481).