Evidence map›Paper›PMID 31239499›Full record

ArticleScientific reports2019

A 3-D Projection Model for X-ray Dark-field Imaging.

Lina Felsner, Shiyang Hu, Andreas Maier, Johannes Bopp, Veronika Ludwig, Gisela Anton, Christian Riess

Abstract read
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Lina FelsnerPattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University of Erlangen-Nuremberg, Erlangen, Germany. lina.felsner@fau.de.ORCID http://orcid.org/0000-0001-7695-2612
Shiyang HuPattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University of Erlangen-Nuremberg, Erlangen, Germany. shiyang.hu@fau.de.
Andreas MaierPattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University of Erlangen-Nuremberg, Erlangen, Germany.ORCID http://orcid.org/0000-0002-9550-5284
Johannes BoppPattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University of Erlangen-Nuremberg, Erlangen, Germany.ORCID http://orcid.org/0000-0001-9419-6697
Veronika LudwigInternational Max Planck Research School, Erlangen, Germany.ORCID http://orcid.org/0000-0003-3711-6958
Gisela AntonInternational Max Planck Research School, Erlangen, Germany.
Christian RiessPattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University of Erlangen-Nuremberg, Erlangen, Germany.ORCID http://orcid.org/0000-0002-5556-5338

Funding

Deutsche Forschungsgemeinschaft (German Research Foundation) AN 257/21-1 | MA 4898/6-1
6 · The paper itself

Abstract

The X-ray dark-field signal can be measured with a grating-based Talbot-Lau interferometer. It measures small angle scattering of micrometer-sized oriented structures. Interestingly, the signal is a function not only of the material, but also of the relative orientation of the sample, the X-ray beam direction, and the direction of the interferometer sensitivity. This property is very interesting for potential tomographically reconstructing structures below the imaging resolution. However, tomographic reconstruction itself is a substantial challenge. A key step of the reconstruction algorithm is the inversion of a forward projection model. In this work, we propose a very general 3-D projection model. We derive the projection model under the assumption that the observed scatter distribution has a Gaussian shape. We theoretically show the consistency of our model with existing, more constrained 2-D models. Furthermore, we experimentally show the compatibility of our model with simulations and real dark-field measurements. We believe that this 3-D projection model is an important step towards more flexible trajectories and, by extension, dark-field imaging protocols that are much better applicable in practice.

Identifiers

PMID31239499
PMCPMC6592954

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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.