Accurate determination of the shape and location of metal objects in x-ray computed tomography

Wang, Jing; Li, Xing

doi:10.1117/12.844294

Cited by 10 publications

(11 citation statements)

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“…It is, of course, not the only way of improving image quality by enhancing sparsity of the IG. In our previous work, [28][29][30][31]33 we used a penalized smoothness (PS) function with an anisotropic prior and showed improved resolution and preserved edges in the reconstructed image. In the PS objective, the prior is adaptively changing with each update of the image during the optimization process.…”

Section: Discussionmentioning

confidence: 99%

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Sequentially reweighted TV minimization for CT metal artifact reduction

Zhang

2013

Medical Physics

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Purpose: Metal artifact reduction has long been an important topic in x-ray CT image reconstruction. In this work, the authors propose an iterative method that sequentially minimizes a reweighted total variation (TV) of the image and produces substantially artifact-reduced reconstructions. Methods: A sequentially reweighted TV minimization algorithm is proposed to fully exploit the sparseness of image gradients (IG). The authors first formulate a constrained optimization model that minimizes a weighted TV of the image, subject to the constraint that the estimated projection data are within a specified tolerance of the available projection measurements, with image non-negativity enforced. The authors then solve a sequence of weighted TV minimization problems where weights used for the next iteration are computed from the current solution. Using the complete projection data, the algorithm first reconstructs an image from which a binary metal image can be extracted. Forward projection of the binary image identifies metal traces in the projection space. The metal-free background image is then reconstructed from the metal-trace-excluded projection data by employing a different set of weights. Each minimization problem is solved using a gradient method that alternates projection-onto-convex-sets and steepest descent. A series of simulation and experimental studies are performed to evaluate the proposed approach. Results: Our study shows that the sequentially reweighted scheme, by altering a single parameter in the weighting function, flexibly controls the sparsity of the IG and reconstructs artifacts-free images in a two-stage process. It successfully produces images with significantly reduced streak artifacts, suppressed noise and well-preserved contrast and edge properties. Conclusions: The sequentially reweighed TV minimization provides a systematic approach for suppressing CT metal artifacts. The technique can also be generalized to other "missing data" problems in CT image reconstruction.

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Section: Discussionmentioning

confidence: 99%

“…The idea of binary reconstruction 30 is to obtain a binary image with the metal objects having high attenuation of 1 and the remaining background tissues having low attenuation of 0. Metal traces in the projection space are then determined by a forward projection of the binary metal image.…”

Section: Iib1 Binary Image Reconstructionmentioning

confidence: 99%

Sequentially reweighted TV minimization for CT metal artifact reduction

Zhang

2013

Medical Physics

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“…͑1͒. 16,17 Once the image is reconstructed, a thresholding operation is applied to binarize the image. By projecting the binary image into the projection domain, the metal-contaminated entries can be identified from projection data and a constrained optimization model is then applied to the remaining projections.…”

Section: ͑3͒mentioning

confidence: 99%

“…We have recently proposed an image reconstruction method capable of autoidentifying the shape and location of metallic object͑s͒ in the image space [16][17][18] based on a penalized weighted least-squares ͑PWLS͒ method. The yielded binary image contains only metal and background and a forward projection of the image to the projection space provides accurate information of the metal corrupted projection data.…”

Section: Introductionmentioning

confidence: 99%

Metal artifact reduction in x‐ray computed tomography (CT) by constrained optimization

2011

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Purpose:The streak artifacts caused by metal implants have long been recognized as a problem that limits various applications of CT imaging. In this work, the authors propose an iterative metal artifact reduction algorithm based on constrained optimization. Methods: After the shape and location of metal objects in the image domain is determined automatically by the binary metal identification algorithm and the segmentation of "metal shadows" in projection domain is done, constrained optimization is used for image reconstruction. It minimizes a predefined function that reflects a priori knowledge of the image, subject to the constraint that the estimated projection data are within a specified tolerance of the available metal-shadow-excluded projection data, with image non-negativity enforced. The minimization problem is solved through the alternation of projection-onto-convex-sets and the steepest gradient descent of the objective function. The constrained optimization algorithm is evaluated with a penalized smoothness objective. Results:The study shows that the proposed method is capable of significantly reducing metal artifacts, suppressing noise, and improving soft-tissue visibility. It outperforms the FBP-type methods and ART and EM methods and yields artifacts-free images. Conclusions: Constrained optimization is an effective way to deal with CT reconstruction with embedded metal objects. Although the method is presented in the context of metal artifacts, it is applicable to general "missing data" image reconstruction problems.

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“…Most of these methods are based on inpainting-based methods (e.g. interpolation [3][4][5][6][7]), normalized interpolation methods [2], Poisson inpainting [8], wavelet [9][10][11], tissue-class models [12] and total variation [13], Euler's elastica [14], iterative reconstruction methods [15][16][17][18] (e.g. iterative FBP, weighted least-square methods) and hybrid methods that combine the first two methods.…”

Section: Introductionmentioning

confidence: 99%

Computed tomographic beam-hardening artefacts: mathematical characterization and analysis

Park

Chung

Seo

2015

Phil. Trans. R. Soc. A.

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One contribution of 11 to a theme issue 'X-ray tomographic reconstruction for materials science' . This paper presents a mathematical characterization and analysis of beam-hardening artefacts in Xray computed tomography (CT). In the field of dental and medical radiography, metal artefact reduction in CT is becoming increasingly important as artificial prostheses and metallic implants become more widespread in ageing populations. Metal artefacts are mainly caused by the beam-hardening of polychromatic X-ray photon beams, which causes mismatch between the actual sinogram data and the data model being the Radon transform of the unknown attenuation distribution in the CT reconstruction algorithm. We investigate the beamhardening factor through a mathematical analysis of the discrepancy between the data and the Radon transform of the attenuation distribution at a fixed energy level. Separation of cupping artefacts from beam-hardening artefacts allows causes and effects of streaking artefacts to be analysed. Various computer simulations and experiments are performed to support our mathematical analysis.

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Accurate determination of the shape and location of metal objects in x-ray computed tomography

Cited by 10 publications

References 0 publications

Sequentially reweighted TV minimization for CT metal artifact reduction

Sequentially reweighted TV minimization for CT metal artifact reduction

Metal artifact reduction in x‐ray computed tomography (CT) by constrained optimization

Computed tomographic beam-hardening artefacts: mathematical characterization and analysis

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