Evidence map›Paper›PMID 31768595›Full record

ReviewAbdominal radiology (New York)2020

CT and MR perfusion techniques to assess diffuse liver disease.

Maxime Ronot, Benjamin Leporq, Bernard E Van Beers, Valérie Vilgrain

Abstract readReview
PubMed Publisher
In one paragraph

Review in Abdominal radiology (New York), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.6field-weighted citation impact, top 16% 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

11 citing papers in PubMed, 23 citations in OpenAlex.

  1. Review
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  11. [Application of Deep Learning Reconstruction Algorithm in Low-Dose Thin-Slice Liver CT of Healthy Volunteers].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2021
    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

4 authors at 1 institution in 1 country.

Maxime RonotDepartment of Radiology, APHP, University Hospitals Paris Nord Val de Seine, Beaujon, Clichy, Hauts-de-Seine, France. maxime.ronot@aphp.fr.ORCID http://orcid.org/0000-0001-7464-3939
Benjamin LeporqDepartment of Radiology, APHP, University Hospitals Paris Nord Val de Seine, Beaujon, Clichy, Hauts-de-Seine, France.
Bernard E Van BeersDepartment of Radiology, APHP, University Hospitals Paris Nord Val de Seine, Beaujon, Clichy, Hauts-de-Seine, France.
Valérie VilgrainDepartment of Radiology, APHP, University Hospitals Paris Nord Val de Seine, Beaujon, Clichy, Hauts-de-Seine, France.
Inserm · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Perfusion imaging allows for the quantitative extraction of physiological perfusion parameters of the liver microcirculation at levels far below the spatial the resolution of CT and MR imaging. Because of its peculiar structure and architecture, perfusion imaging is more challenging in the liver than in other organs. Indeed, the liver is a mobile organ and significantly deforms with respiratory motion. Moreover, it has a dual vascular supply and the sinusoidal capillaries are fenestrated in the normal liver. Using extracellular contrast agents, perfusion imaging has shown its ability to discriminate patients with various stages of liver fibrosis. The recent introduction of hepatobiliary contrast agents enables quantification of both the liver perfusion and the hepatocyte transport function using advanced perfusion models. The purpose of this review article is to describe the characteristics of liver perfusion imaging to assess chronic liver disease, with a special focus on CT and MR imaging.

Indexed as

Liver DiseasesContrast MediaHumansLiverLiver CirrhosisMagnetic Resonance ImagingPerfusionTomography, X-Ray ComputedContrast MediaFibrosisHepatic arteryLiver cirrhosisMicrocirculationPerfusion imaging

Identifiers

PMID31768595
OpenAlexW2990107339

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.