Segmentation of artifacts and anatomy in CT metal artifact reduction

Karimi, Seemeen; Cosman, Pamela C.; Wald, Christoph; Martz, H.E.

doi:10.1118/1.4749931

Cited by 58 publications

(55 citation statements)

References 29 publications

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“…The accuracy of the prior image is of great importance for the performance of both NMAR and the proposed algorithm, since segmentation errors in prior image can reappear in final reconstructions. For accurate segmentation of artifacts from anatomy in a prior image, automatic procedures through adaptive and knowledge-based thresholding have been described in [59] and [60], respectively.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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“…In the segmentation of uncorrected CT images into different tissues, severe dark and bright streaking artifacts can be falsely classified as air and bone in the segmented soft tissue and bone images, respectively. Following the work of Karimi et al [53] on the derivation of a prior image, we applied a 3D close and open morphological filtering on the segmented classes to reduce these errors. In cases with severe artifacts, the residual misclassifications were interactively reduced using a graphical user interface.…”

Section: Prior and Metal-only Imagesmentioning

confidence: 99%

3D Prior Image Constrained Projection Completion for X-ray CT Metal Artifact Reduction

Mehranian

Rahmim

et al. 2013

IEEE Trans. Nucl. Sci.

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Abstract-The presence of metallic implants in the body of patients undergoing X-ray computed tomography (CT) examinations often results in severe streaking artifacts that degrade image quality. In this work, we propose a new metal artifact reduction (MAR) algorithm for 2D fan-beam and 3D cone-beam CT based on the maximum a posteriori (MAP) completion of the projections corrupted by metallic implants. In this algorithm, the prior knowledge obtained from a tissue-classified prior image is exploited in the completion of missing projections and incorporated into a new prior potential function. The prior is especially designed to exploit and promote the sparsity of a residual projection (sinogram) dataset obtained from the subtraction of the unknown target dataset from the projection dataset of the tissue-classified prior image. The MAP completion is formulated as an equality-constrained convex optimization and solved using an accelerated projected gradient algorithm. The performance of the proposed algorithm is compared with two state-of-the-art algorithms, namely 3D triangulated linear interpolation (LI) and normalized metal artifact reduction (NMAR) algorithm using simulated and clinical studies. The simulations targeting artifact reduction in 2D fan-beam and 3D cone-beam CT demonstrate that our algorithm can outperform its counterparts, particularly in cone-beam CT. In the clinical datasets, the performance of the proposed algorithm was subjectively and objectively compared in terms of metal artifact reduction of a sequence of 2D CT slices. The clinical results show that the proposed algorithm effectively reduces metal artifacts without introducing new artifacts due to erroneous interpolation and normalization as in the case of LI and NMAR algorithms.Index Terms-Metal artifact reduction, prior image, X-ray CT, 3D projection completion.

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“…This prior information allows for effective parameter tuning and can be used to 'guide' the interpolation process [3]. Those MAR techniques that rely on effective parameter tuning and/or prior information [13,18,19,20,21,22,23] are however, expected to be of limited value in the security screening domain, where the contents of the scans are inherently unpredictable making the generation of prior information and effective parameter tuning more challenging.…”

Section: Introductionmentioning

confidence: 99%

“…Pre-and post-filtering techniques [15,16], feedback strategies [17], guided sinogram completion techniques based on priors obtained via multiclass segmentation [13,18,19,20,21], wavelet techniques [22,23], Fourier-based interpolation [24] and region-specific interpolation [15,16] have been proposed and met with varying degrees of success. With the exception of [16], these methods are all intended for use in the medical field.…”

Section: Introductionmentioning

confidence: 99%

A distance driven method for metal artefact reduction in computed tomography

Mouton

Megherbi

Slambrouck

et al. 2013

2013 IEEE International Conference on Image Processing

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This paper presents an extension to a recent intensity-limiting sinogram completion-based Metal Artefact Reduction (MAR) algorithm for Computed Tomography (CT) images containing multiple metal objects. A novel weighting scheme is introduced, whereby the intensities of the MAR-corrected pixels are modified based on their spatial locations relative to the metal objects. Pixels falling within the straight-line regions connecting multiple metal objects are subjected to less intensive intensity-limiting, thereby compensating for the characteristic dark bands occurring in these regions. Extensive experimentation is performed on a state-of-the-art numerical simulation, a clinical CT data set and a baggage security CT data set. Comprehensive performance analysis, using reference and reference-free error metrics, Bland-Altman plots and visual comparisons, demonstrate an improvement in the restoration of the underestimated intensities occurring in the regions connecting multiple metal objects.

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Segmentation of artifacts and anatomy in CT metal artifact reduction

Abstract: Metals produce predictable artifacts in CT images of the head. Using the new method, metal artifacts can be discriminated from anatomy, and the discrimination can be used to reduce metal artifacts.

Cited by 58 publications

References 29 publications

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

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

3D Prior Image Constrained Projection Completion for X-ray CT Metal Artifact Reduction

A distance driven method for metal artefact reduction in computed tomography

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