Prior-Guided Metal Artifact Reduction for Iterative X-Ray Computed Tomography

Chang, Zhiqian; Ye, Dong Hye; Srivastava, Sudhanshu; Thibault, Jean‐Baptiste; Sauer, Ken D.; Bouman, Charles A.

doi:10.1109/tmi.2018.2886701

Cited by 31 publications

(16 citation statements)

References 43 publications

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“…Xi et al [27] used High-kVp to reduce beam hardening effect of lower-kVP x rays. Chang et al [28] proposed a prior-based iterative method to reduce metal artifacts. It combines the superiority of statistical methods with the sinogram completion methods to estimate and correct metal-induced biases.…”

Section: Iterative Reconstructionmentioning

confidence: 99%

“…It combines the superiority of statistical methods with the sinogram completion methods to estimate and correct metal-induced biases. Chang et al [28] using Convolutional neural network(CNN) to obtain prior image. He simulated the metal artifacts generation on CT images without metal artifacts and then train the CNN net from original metal artifact contaminated image to prior image.…”

Section: Iterative Reconstructionmentioning

confidence: 99%

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Metal Artifact Reduction Method Based on Noncoplanar Scanning in CBCT Imaging

et al. 2020

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This study proposed a metal artifact reduction (MAR) method based on noncoplanar scanning for cone beam computed tomography (CBCT) imaging. Cylindrical aluminum and stainless steel bars were inserted into a phantom for cross-sectional and sagittal CBCT scanning. The metal bars were removed from scanning to obtain real CBCT images without metal artifacts. Corrected CBCT images were acquired after the artifact reduction of cross-sectional and sagittal images. Real CBCT images were used as the criteria, and the computed tomography value deviations of the original cross-sectional, sagittal, linear interpolation MAR (LIMAR), normalized MAR (NMAR), beam hardening correction (BHC) and proposed method were calculated. Most metal artifacts in the original image are removed in the corrected image, and thus, image quality is remarkably improved. The root mean square deviations of the original cross-sectional, sagittal, LIMAR, NMAR,BHC and proposed method are 122, 116, 90, 103, 92 and 85 HU, respectively, whereas their mean absolute deviations are 71, 73, 57, 55, 58 and 51 HU, respectively. The CT value deviation of the proposed method is the minimum. Conclusion: The proposed MAR method based on noncoplanar scanning can effectively reduce metal artifacts and improve CBCT image quality.

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Section: Iterative Reconstructionmentioning

confidence: 99%

Section: Iterative Reconstructionmentioning

confidence: 99%

Metal Artifact Reduction Method Based on Noncoplanar Scanning in CBCT Imaging

et al. 2020

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“…Segmentation in the inpainting methods can be avoided by using histogram deformation [6]. Some authors try to model the polychromatic energy spectrum into the algorithm [7][8][9]. Using dual-energy CT is able to better synthesize virtual monochromatic images at different photon energy levels, and virtual monochromatic images obtained at high kiloelectron volt levels are known to reduce the effects of beam hardening [8,9].…”

Section: Introductionmentioning

confidence: 99%

“…The current metal artifact reduction methods can be roughly divided into two categories. In the first category [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20], the affected sinogram data is replaced by the estimated data. The estimated data is obtained by using its neighboring measurements and/or by X-ray beam hardening models.…”

Section: Introductionmentioning

confidence: 99%

Projection-domain iteration to estimate unreliable measurements

Zeng

2020

Vis. Comput. Ind. Biomed. Art

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Due to the beam-hardening effect of the broad energy spectrum of the X-ray source in computed tomography, the reconstructed images usually suffer from severe artifacts when metallic objects are being imaged. Metal artifact correction methods are usually sophisticated and not practical, especially in some non-medical applications, in which the linear attenuation coefficients are unknown. This paper suggests a simple and effective algorithm to estimate the unreliable measurements. The proposed algorithm is an iterative algorithm, in which the iteration is performed in the projection domain, while the objective function is set up in the image domain. The final image is reconstructed with the conventional filtered backprojection algorithm. The feasibility of the proposed method is verified with airport bags that contain some unknown metals.

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“…Later work has incorporated polyenergetic modeling into the EM framework, 28,29 applied EM to projection completed data, 30 used ART in conjunction with total variation (TV) penalties 31,32 and used optimization‐based reconstruction with machine‐learned regularization 24 . In Reference [33] the authors simultaneously estimate the image and the mismatch between polyenergetic and (idealized) monoenergetic data, using a regularized least squares approach incorporating a prior image and several different regularizers.…”

Section: Introductionmentioning

confidence: 99%

Superiorized method for metal artifact reduction

Humphries

Wang

2020

Medical Physics

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Purpose: Metal artifact reduction (MAR) is a challenging problem in computed tomography (CT) imaging. A popular class of MAR methods replace sinogram measurements that are corrupted by metal with artificial data, typically generated from some combination of interpolation along with other heuristics. While these "projection completion" approaches are successful in eliminating severe artifacts, secondary artifacts may be introduced by the artificial data. In this paper, we propose an approach which uses projection completion to generate a prior image, which is then incorporated into an iterative reconstruction algorithm based on the superiorization framework. The rationale is that the image produced by the iterative algorithm can inherit the desirable properties of the prior image, while also reducing secondary artifacts. Methods: The prior image is reconstructed using normalized metal artifact reduction (NMAR), a popular projection completion approach. The iterative algorithm is a modified version of the simultaneous algebraic reconstruction technique (SART), which reduces artifacts by incorporating a polyenergetic forward model, least-squares weighting, and superiorization. The penalty function used for superiorization is a weighted average between a total variation (TV) term and a term promoting similarity with the prior image, similar to penalty functions used in prior image constrained compressive sensing (PICCS). Because the prior is largely free of severe metal artifacts, these artifacts are discouraged from arising during iterative reconstruction; additionally, because the iterative approach uses the original projection data, it is able to recover information that is lost during the NMAR process. Results: We perform numerical experiments modeling a simple geometric object, as well as several more realistic scenarios such as metal pins, bilateral hip implants, and dental fillings placed within an anatomical phantom. The proposed iterative algorithm is largely successful at eliminating severe metal artifacts as well as secondary artifacts introduced by the NMAR process, especially lost edges of bone structures in the neighborhood of the metal regions. In one case modeling severe photon starvation, the NMAR algorithm is found to provide better results. Conclusion: The proposed algorithm is effective in applying the superiorization methodology to the problem of MAR, providing better results than both NMAR and a purely total variation-based superiorization approach in nearly all cases.

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Prior-Guided Metal Artifact Reduction for Iterative X-Ray Computed Tomography

Cited by 31 publications

References 43 publications

Metal Artifact Reduction Method Based on Noncoplanar Scanning in CBCT Imaging

Metal Artifact Reduction Method Based on Noncoplanar Scanning in CBCT Imaging

Projection-domain iteration to estimate unreliable measurements

Superiorized method for metal artifact reduction

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