Evidence map›Paper›PMID 34218672›Full record

ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2021

Synergistic tomographic image reconstruction: part 2.

Charalampos Tsoumpas, Jakob Sauer Jørgensen, Christoph Kolbitsch, Kris Thielemans

Abstract read
In one paragraph

Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Charalampos TsoumpasBiomedical Imaging Science Department, University of Leeds, West Yorkshire, UK.
Jakob Sauer JørgensenDepartment of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark.
Christoph KolbitschPhysikalisch-Technische Bundesanstalt, Braunschweig and Berlin, Germany.
Kris ThielemansInstitute of Nuclear Medicine, University College London, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This special issue is the second part of a themed issue that focuses on synergistic tomographic image reconstruction and includes a range of contributions in multiple disciplines and application areas. The primary subject of study lies within inverse problems which are tackled with various methods including statistical and computational approaches. This volume covers algorithms and methods for a wide range of imaging techniques such as spectral X-ray computed tomography (CT), positron emission tomography combined with CT or magnetic resonance imaging, bioluminescence imaging and fluorescence-mediated imaging as well as diffuse optical tomography combined with ultrasound. Some of the articles demonstrate their utility on real-world challenges, either medical applications (e.g. motion compensation for imaging patients) or applications in material sciences (e.g. material decomposition and characterization). One of the desired outcomes of the special issues is to bring together different scientific communities which do not usually interact as they do not share the same platforms such as journals and conferences. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.

Indexed as

AlgorithmsHumansImage Interpretation, Computer-AssistedMotionMultimodal ImagingRadiographic Image Interpretation, Computer-AssistedSoftwareTomographyTomography, X-Ray Computeddiffuse optical tomographymagnetic resonance imagingopen-source softwarepositron emission tomographytomographyX-ray computed tomography

Identifiers

PMID34218672
PMCPMC8255945

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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.