Evidence map›Paper›PMID 36792717›Full record

ArticleScientific reports2023

The choice of an autocorrelation length in dark-field lung imaging.

Simon Spindler, Dominik Etter, Michał Rawlik, Maxim Polikarpov, Lucia Romano, Zhitian Shi, Konstantins Jefimovs, Zhentian Wang, Marco Stampanoni

Abstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 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

9 authors.

Simon Spindler *Swiss Light Source, Paul Scherrer Institute, 5232, Villigen, Switzerland. simon.spindler@psi.ch.
Dominik Etter *Swiss Light Source, Paul Scherrer Institute, 5232, Villigen, Switzerland.
Michał RawlikSwiss Light Source, Paul Scherrer Institute, 5232, Villigen, Switzerland.
Maxim PolikarpovSwiss Light Source, Paul Scherrer Institute, 5232, Villigen, Switzerland.
Lucia RomanoSwiss Light Source, Paul Scherrer Institute, 5232, Villigen, Switzerland.
Zhitian ShiSwiss Light Source, Paul Scherrer Institute, 5232, Villigen, Switzerland.
Konstantins JefimovsSwiss Light Source, Paul Scherrer Institute, 5232, Villigen, Switzerland.
Zhentian WangInstitute for Biomedical Engineering, ETH Zürich, 8092, Zürich, Switzerland.
Marco StampanoniSwiss Light Source, Paul Scherrer Institute, 5232, Villigen, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Respiratory diseases are one of the most common causes of death, and their early detection is crucial for prompt treatment. X-ray dark-field radiography (XDFR) is a promising tool to image objects with unresolved micro-structures such as lungs. Using Talbot-Lau XDFR, we imaged inflated porcine lungs together with Polymethylmethacrylat (PMMA) microspheres (in air) of diameter sizes between 20 and 500 [Formula: see text] over an autocorrelation range of 0.8-5.2 [Formula: see text]. The results indicate that the dark-field extinction coefficient of porcine lungs is similar to that of densely-packed PMMA spheres with diameter of [Formula: see text], which is approximately the mean alveolar structure size. We evaluated that, in our case, the autocorrelation length would have to be limited to [Formula: see text] in order to image [Formula: see text]-thick lung tissue without critical visibility reduction (signal saturation). We identify the autocorrelation length to be the critical parameter of an interferometer that allows to avoid signal saturation in clinical lung dark-field imaging.

Indexed as

LungPolymethyl MethacrylateAnimalsRadiographySwineX-RaysPolymethyl Methacrylate

Identifiers

PMID36792717
PMCPMC9932147

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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.