Evidence map›Paper›PMID 40685465›Full record

ArticleEJNMMI reports2025

Ultra-low dose imaging in a standard axial field-of-view PET.

Thiago Lima, Carlos Vinícius Gomes, Paul Fargier, Klaus Strobel, Antoine Leimgruber

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in EJNMMI reports, 2025. 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

5 authors.

Thiago LimaDepartment of Radiology and Nuclear Medicine, Luzerner Kantonsspital, University Teaching and Research Hospital, University of Lucerne, Lucerne, Switzerland.ORCID http://orcid.org/0000-0002-3105-9270
Carlos Vinícius GomesDepartment of Radiology and Nuclear Medicine, Luzerner Kantonsspital, University Teaching and Research Hospital, University of Lucerne, Lucerne, Switzerland.ORCID http://orcid.org/0000-0001-6978-338X
Paul FargierDepartment of Nuclear Medicine, Swiss Medical Network, Clinique de Genolier Rte du Muids 3, Genolier, 1272, Switzerland.
Klaus StrobelDepartment of Radiology and Nuclear Medicine, Luzerner Kantonsspital, University Teaching and Research Hospital, University of Lucerne, Lucerne, Switzerland.ORCID http://orcid.org/0000-0002-3369-4665
Antoine LeimgruberDepartment of Radiology and Nuclear Medicine, Luzerner Kantonsspital, University Teaching and Research Hospital, University of Lucerne, Lucerne, Switzerland. aleimgruber@swissmedical.net.ORCID http://orcid.org/0009-0008-0994-1890

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Though ultra-low dose (ULD) imaging offers notable benefits, its widespread clinical adoption faces challenges. Long-axial field-of-view (LAFOV) PET/CT systems are expensive and scarce, while artificial intelligence (AI) shows great potential but remains largely limited to specific systems and is not yet widely used in clinical practice. However, integrating AI techniques and technological advancements into ULD imaging is helping bridge the gap between standard axial field-of-view (SAFOV) and LAFOV PET/CT systems. This paper offers an initial evaluation of ULD capabilities using one of the latest SAFOV PET/CT device. A patient injected with 16.4 MBq

Indexed as

BGOPETStandard field-of-viewUltra-low dose

Identifiers

PMID40685465
PMCPMC12277232

What OpenQuestion holds

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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.