ArticleVisual computing for industry, biomedicine, and art2021
Reducing metal artifacts by restricting negative pixels.
Article in Visual computing for industry, biomedicine, and art, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Neural network guided sinogram-domain iterative algorithm for artifact reduction.Medical physics · 2023Article
- A projection-domain iterative algorithm for metal artifact reduction by minimizing the total-variation norm and the negative-pixel energy.Visual computing for industry, biomedicine, and art · 2022Article
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Authors and funding
2 authors.
Funding
Abstract
When the object contains metals, its x-ray computed tomography (CT) images are normally affected by streaking artifacts. These artifacts are mainly caused by the x-ray beam hardening effects, which deviate the measurements from their true values. One interesting observation of the metal artifacts is that certain regions of the metal artifacts often appear as negative pixel values. Our novel idea in this paper is to set up an objective function that restricts the negative pixel values in the image. We must point out that the naïve idea of setting the negative pixel values in the reconstructed image to zero does not give the same result. This paper proposes an iterative algorithm to optimize this objective function, and the unknowns are the metal affected projections. Once the metal affected projections are estimated, the filtered backprojection algorithm is used to reconstruct the final image. This paper applies the proposed algorithm to some airport bag CT scans. The bags all contain unknown metallic objects. The metal artifacts are effectively reduced by the proposed algorithm.
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Registered trials
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