Evidence map›Paper›PMID 31158251›Full record

ArticlePloS one2019

Contrast-to-noise ratios and thickness-normalized, ventilation-dependent signal levels in dark-field and conventional in vivo thorax radiographs of two pigs.

Fabio De Marco, Konstantin Willer, Lukas B Gromann, Jana Andrejewski, Katharina Hellbach, Andrea Bähr, Michaela Dmochewitz, Thomas Koehler, Hanns-Ingo Maack, Franz Pfeiffer and 1 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
3.0field-weighted citation impact, top 9% of its field
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

9 citing papers in PubMed, 15 citations in OpenAlex.

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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

11 authors at 6 institutions in 1 country.

Fabio De MarcoChair of Biomedical Physics & School of BioMedical Engineering, Technical University of Munich, Garching, Germany.ORCID 0000-0002-3561-7305
Konstantin WillerChair of Biomedical Physics & School of BioMedical Engineering, Technical University of Munich, Garching, Germany.
Lukas B GromannChair of Biomedical Physics & School of BioMedical Engineering, Technical University of Munich, Garching, Germany.
Jana AndrejewskiChair of Biomedical Physics & School of BioMedical Engineering, Technical University of Munich, Garching, Germany.
Katharina HellbachDepartment of Radiology, University Hospital, LMU Munich, Munich, Germany.
Andrea BährInstitute of Molecular Animal Breeding and Biotechnology, LMU Munich, Oberschleissheim, Germany.
Michaela DmochewitzInstitute of Molecular Animal Breeding and Biotechnology, LMU Munich, Oberschleissheim, Germany.
Thomas KoehlerPhilips GmbH Innovative Technologies, Research Laboratories, Hamburg, Germany.ORCID 0000-0002-1986-0057
Hanns-Ingo MaackPhilips Medical Systems DMC GmbH, Hamburg, Germany.
Franz PfeifferChair of Biomedical Physics & School of BioMedical Engineering, Technical University of Munich, Garching, Germany.
Julia HerzenChair of Biomedical Physics & School of BioMedical Engineering, Technical University of Munich, Garching, Germany.
Technical University of Munich · DEInstitute of Bioinformatics and Systems Biology · DEInstitute for Advanced Study · DELMU Klinikum · DEPhilips (Germany) · DETUM Klinikum · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung tissue causes significant small-angle X-ray scattering, which can be visualized with grating-based X-ray dark-field imaging. Structural lung diseases alter alveolar microstructure, which often causes a dark-field signal decrease. The imaging method provides benefits for diagnosis of such diseases in small-animal models, and was successfully used on porcine and human lungs in a fringe-scanning setup. Micro- and macroscopic changes occur in the lung during breathing, but their individual effects on the dark-field signal are unknown. However, this information is important for quantitative medical evaluation of dark-field thorax radiographs. To estimate the effect of these changes on the dark-field signal during a clinical examination, we acquired in vivo dark-field chest radiographs of two pigs at three ventilation pressures. Pigs were used due to the high degree of similarity between porcine and human lungs. To analyze lung expansion separately, we acquired CT scans of both pigs at comparable posture and ventilation pressures. Segmentation, masking, and forward-projection of the CT datasets yielded maps of lung thickness and logarithmic lung attenuation signal in registration with the dark-field radiographs. Upon correlating this data, we discovered approximately linear relationships between the logarithmic dark-field signal and both projected quantities for all scans. Increasing ventilation pressure strongly decreased dark-field extinction coefficients, whereas the ratio of lung dark-field and attenuation signal changed only slightly. Furthermore, we investigated ratios of dark-field and attenuation noise levels at realistic signal levels via calculations and phantom measurements. Dark-field contrast-to-noise ratio (CNR) per lung height was 5 to 10% of the same quantity in attenuation. We conclude that better CNR performance in the dark-field modality is typically due to greater anatomical noise in the conventional radiograph. Given the high physiological similarity of human and porcine lungs, the presented thickness-normalized, ventilation-dependent values allow estimation of dark-field activity of human lungs of variable size and inspiration, which facilitates the design of suitable clinical imaging setups.

Indexed as

Signal-To-Noise RatioVentilationAnimalsComputer SimulationContrast MediaImage Processing, Computer-AssistedLinear ModelsSwineThoraxTomography, X-Ray ComputedContrast Media

Identifiers

PMID31158251
PMCPMC6546243
OpenAlexW2948289379

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Registered trials

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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.