Development and validation of segmentation and interpolation techniques in sinograms for metal artifact suppression in CT

Veldkamp, Wouter J. H.; Joemai, Raoul M. S.; Molen, Aart J. van der; Geleijns, Jacob

doi:10.1118/1.3276777

Cited by 114 publications

(86 citation statements)

References 19 publications

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“…There are three classes of metal artifact reduction methods: sinogram in-painting [1][2][3][4][5][6][7], iterative reconstruction [8][9][10], and energy decomposition [11][12][13]. Iterative reconstruction requires accurate modeling of the X-ray generation and attenuation processes, which is difficult to accomplish.…”

Section: B Current Approachesmentioning

confidence: 99%

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Using Segmentation in CT Metal Artifact Reduction

Karimi

Cosman

Wald

et al. 2012

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Abstract-Metal artifact reduction methods in computed tomography replace the projection data passing through metals with an estimate of the true data. Inaccurate estimation leads to the generation of secondary artifacts. Data estimates can be improved by the use of prior knowledge of the projection data. In this paper, a method has been created to generate a prior image. The method uses computer vision techniques to segment regions of the initially reconstructed image and then discriminates between regions that are likely to be artifacts and anatomical structures. Results on test images show that metal artifacts are reduced and that few secondary artifacts are present, even in the case of multiple metal objects.

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Section: B Current Approachesmentioning

confidence: 99%

“…In some methods [1][2][3][4][5][6], metal objects are located in the original image by thresholding, and the traces are located by calculation. In other methods [7], the metal traces are located directly in projections. Early work [1,2] interpolated the projection data on either side of the traces.…”

Section: B Current Approachesmentioning

confidence: 99%

Using Segmentation in CT Metal Artifact Reduction

Karimi

Cosman

Wald

et al. 2012

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“…Subsequent work by Yu et al [34] does the metal segmentation directly in the projections. Veldkamp et al [30] applies a complex segmentation strategy in projection space. Three interpolation methods are investigated.…”

Section: Related Workmentioning

confidence: 99%

Projection-Based Metal-Artifact Reduction for Industrial 3D X-ray Computed Tomography

Amirkhanov

Heinzl

Reiter

et al. 2011

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. The plug specimen photo (left) and volume renderings before (center) and after (right) metal-artifact reduction.Abstract-Multi-material components, which contain metal parts surrounded by plastic materials, are highly interesting for inspection using industrial 3D X-ray computed tomography (3DXCT). Examples of this application scenario are connectors or housings with metal inlays in the electronic or automotive industry. A major problem of this type of components is the presence of metal, which causes streaking artifacts and distorts the surrounding media in the reconstructed volume. Streaking artifacts and dark-band artifacts around metal components significantly influence the material characterization (especially for the plastic components). In specific cases these artifacts even prevent a further analysis. Due to the nature and the different characteristics of artifacts, the development of an efficient artifact-reduction technique in reconstruction-space is rather complicated. In this paper we present a projection-space pipeline for metal-artifacts reduction. The proposed technique first segments the metal in the spatial domain of the reconstructed volume in order to separate it from the other materials. Then metal parts are forward-projected on the set of projections in a way that metal-projection regions are treated as voids. Subsequently the voids, which are left by the removed metal, are interpolated in the 2D projections. Finally, the metal is inserted back into the reconstructed 3D volume during the fusion stage. We present a visual analysis tool, allowing for interactive parameter estimation of the metal segmentation. The results of the proposed artifact-reduction technique are demonstrated on a test part as well as on real world components. For these specimens we achieve a significant reduction of metal artifacts, allowing an enhanced material characterization.

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“…In the projection completion approach, which is often followed by filtered-back-projection (FBP) image reconstruction, the missing projections are synthesized from neighboring projections using linear and polynomial interpolation [4], [8]- [10], wavelet interpolation [11], adaptive filtering [12], [13], and inpainting [14]- [17] techniques. Other approaches aim at replacing the missing projections with those available in opposite view angles or adjacent CT slices [18]- [20] or the projections obtained from forward projection of tissue-segmented CT images [21]- [24].…”

mentioning

confidence: 99%

“…In general, projection completion algorithms should be preceded by a metal trace identification step, in which projections running through metallic implants (i.e., corrupted or missing projections) are identified. This is achieved by either 1) segmentation of CT images for metallic implants and forward projection of the resulting metal-only image into an artificial sinogram domain [4], [26], [27], or 2) direct segmentation of raw sinogram data for corrupted projections [10], [13], [16].…”

mentioning

confidence: 99%

X-ray CT Metal Artifact Reduction Using Wavelet Domain <formula formulatype="inline"><tex Notation="TeX">$L_{0}$</tex></formula> Sparse Regularization

Mehranian

Rahmim

et al. 2013

IEEE Trans. Med. Imaging

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Abstract-X-ray computed tomography (CT) imaging of patients with metallic implants usually suffers from streaking metal artifacts. In this paper, we propose a new projection completion metal artifact reduction (MAR) algorithm by formulating the completion of missing projections as a regularized inverse problem in the wavelet domain. The Douglas-Rachford splitting (DRS) algorithm was used to iteratively solve the problem. Two types of prior information were exploited in the algorithm: 1) the sparsity of the wavelet coefficients of CT sinograms in a dictionary of translation-invariant wavelets and 2) the detail wavelet coefficients of a prior sinogram obtained from the forward projection of a segmented CT image. A pseudosynthesis prior was utilized to exploit and promote the sparsity of wavelet coefficients. The proposed -DRS MAR algorithm was compared with standard linear interpolation and the normalized metal artifact reduction (NMAR) approach proposed by Meyer et al. using both simulated and clinical studies including hip prostheses, dental fillings, spine fixation and electroencephalogram electrodes in brain imaging. The qualitative and quantitative evaluations showed that our algorithm substantially suppresses streaking artifacts and can outperform both linear interpolation and NMAR algorithms.Index Terms-Metal artifact reduction (MAR), streaking artifacts, wavelets, X-ray computed tomography (CT), sparse regularization.

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Development and validation of segmentation and interpolation techniques in sinograms for metal artifact suppression in CT

Cited by 114 publications

References 19 publications

Using Segmentation in CT Metal Artifact Reduction

Using Segmentation in CT Metal Artifact Reduction

Projection-Based Metal-Artifact Reduction for Industrial 3D X-ray Computed Tomography

X-ray CT Metal Artifact Reduction Using Wavelet Domain <formula formulatype="inline"><tex Notation="TeX">$L_{0}$</tex></formula> Sparse Regularization

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