Evidence map›Paper›PMID 34059962›Full record

ArticleVisual computing for industry, biomedicine, and art2021

Reducing metal artifacts by restricting negative pixels.

Gengsheng L Zeng, Megan Zeng

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Gengsheng L ZengDepartment of Computer Science, Utah Valley University, Orem, UT, 84058, USA. larry.zeng@uvu.edu.
Megan ZengDepartment of Electrical Engineering and Computer Science, University of California at Berkeley, Berkeley, CA, 94720, USA.

Funding

Reducing Metal Artifacts in Clinical X-Ray CT via Image Reconstruction TechniquesR15EB024283 · NIBIB · WEBER STATE UNIVERSITY · PI ZENG, GENGSHENG · 2018 to 2022
$644k
NIH HHS R15EB024283
6 · The paper itself

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.

Indexed as

Filtered backprojection image reconstructionIterative algorithmMetal artifact reductionObjective functionX-ray computed tomography

Identifiers

PMID34059962
PMCPMC8166984

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.