Evidence map›Paper›PMID 40235599›Full record

ArticlePhysical review applied2023

Edge-Illumination X-Ray Dark-Field Tomography.

Adam Doherty, Savvas Savvidis, Carlos Navarrete-León, Mattia F M Gerli, Alessandro Olivo, Marco Endrizzi

Abstract read
In one paragraph

Article in Physical review applied, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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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

6 authors.

Adam DohertyDepartment of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, United Kingdom.ORCID https://orcid.org/0000-0002-1673-0337
Savvas SavvidisDepartment of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, United Kingdom.
Carlos Navarrete-LeónDepartment of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, United Kingdom.ORCID https://orcid.org/0000-0003-3301-1964
Mattia F M GerliUCL Division of Surgery and Interventional Science, Royal Free Hospital, London NW3 2PF, United Kingdom.ORCID https://orcid.org/0000-0002-9331-7394
Alessandro OlivoDepartment of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, United Kingdom.ORCID https://orcid.org/0000-0002-7150-2951
Marco EndrizziDepartment of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, United Kingdom.ORCID https://orcid.org/0000-0002-7810-2301

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dark-field imaging is an x-ray technique used to highlight subpixel, typically micrometer-scale, density fluctuations. It is often used alongside standard attenuation-based and also phase-contrast x-ray imaging, which both see regular use in tomography. We present x-ray dark-field computed tomography (CT) with a laboratory edge-illumination setup. The dark-field contrast is shown to increase linearly with the x-ray path length through the imaged object, a prerequisite for the use of standard tomographic reconstruction approaches. A multimaterial, custom-built phantom is used to show how dark-field contrast CT can complement attenuation contrast CT for the separation of materials based on their microstructure. As an example of a more complex, biological sample, we present a model rat heart. We show, by comparison with attenuation contrast tomography, that dark-field enables the identification of additional structures undetected through the attenuation contrast channel, as well as offering a consistently sharper reconstructed image.

Identifiers

PMID40235599
PMCPMC7617590

What OpenQuestion holds

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LicenceCC BY
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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.