Evidence map›Paper›PMID 33041852›Full record

ArticleFrontiers in physiology2020

Monte Carlo Simulations of the GE Signa PET/MR for Different Radioisotopes.

Paulo R R V Caribé, Stefaan Vandenberghe, André Diogo, David Pérez-Benito, Nikos Efthimiou, Charlotte Thyssen, Yves D'Asseler, Michel Koole

Open access · goldAbstract read
In one paragraph

Article in Frontiers in physiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.8field-weighted citation impact, top 24% 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

7 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
  2. Article
  3. Reduction of [EJNMMI physics · 2024
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  4. Review
  5. Application of Metabolic Reprogramming to Cancer Imaging and Diagnosis.International journal of molecular sciences · 2022
    Review
  6. Article
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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

8 authors at 5 institutions in 4 countries.

Paulo R R V CaribéMedical Imaging and Signal Processing - MEDISIP, Ghent University, Ghent, Belgium.
Stefaan VandenbergheMedical Imaging and Signal Processing - MEDISIP, Ghent University, Ghent, Belgium.
André DiogoFaculty of Sciences of the University of Lisbon (FCUL), Lisbon, Portugal.
David Pérez-BenitoBioengineering and Aerospace Department, Universidad Carlos III de Madrid, Madrid, Spain.
Nikos EfthimiouDepartment of Physics, University of York, York, United Kingdom.
Charlotte ThyssenMedical Imaging and Signal Processing - MEDISIP, Ghent University, Ghent, Belgium.
Yves D'AsselerDepartment of Diagnostic Sciences, Faculty of Medicine, Ghent University, Ghent, Belgium.
Michel KooleNuclear Medicine and Molecular Imaging, Department of Imaging & Pathology, KU Leuven, Leuven, Belgium.
Ghent University · BEKU Leuven · BEUniversidad Carlos III de Madrid · ESUniversity of Lisbon · PTUniversity of York · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

NEMA characterization of PET systems is generally based on

methodsAim of this work was three-fold: (A) Develop a GATE model of the GE Signa PET/MR to perform realistic and relevant Monte Carlo simulations (B) Validate this model with published sensitivity and Noise Equivalent Count Rate (NECR) data for

resultsSimulated sensitivity and NECR tests performed with the GATE-model for different radioisotopes were in line with literature values. Simulated sensitivities for

conclusionMonte-Carlo simulations were used to predict sensitivity and NECR performance of GE Signa PET/MR for

Indexed as

high energy positron emittersNEMA NU 2–2012nuclear medicinePET/MRpositron range

Identifiers

PMID33041852
PMCPMC7522581
OpenAlexW3084480418

What OpenQuestion holds

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LicenceCC BY
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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.