Evidence map›Paper›PMID 42254326›Full record

ArticleJournal of biomedical optics2026

Real-time fidelity assessment of fluorescence molecular imaging without reference images.

Elena Kriukova, Sarah Glasl, Vasilis Ntziachristos, Dimitris Gorpas

Abstract read
In one paragraph

Article in Journal of biomedical optics, 2026. 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.

Elena KriukovaTechnical University of Munich, Central Institute for Translational Cancer Research (TranslaTUM), Information and Technology, School of Medicine and Health & School of Computation, Chair of Biological Imaging, Munich, Germany.
Sarah GlaslTechnical University of Munich, Central Institute for Translational Cancer Research (TranslaTUM), Information and Technology, School of Medicine and Health & School of Computation, Chair of Biological Imaging, Munich, Germany.
Vasilis NtziachristosTechnical University of Munich, Central Institute for Translational Cancer Research (TranslaTUM), Information and Technology, School of Medicine and Health & School of Computation, Chair of Biological Imaging, Munich, Germany.ORCID https://orcid.org/0000-0002-9988-0233
Dimitris GorpasTechnical University of Munich, Central Institute for Translational Cancer Research (TranslaTUM), Information and Technology, School of Medicine and Health & School of Computation, Chair of Biological Imaging, Munich, Germany.ORCID https://orcid.org/0000-0001-9576-8087

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Significance: Fluorescence molecular imaging (FMI) has been approved for surgical guidance in several procedures. However, its interpretation still requires substantial clinician expertise due to variable signal levels. For FMI to gain wider clinical acceptance, image quality should be assessed automatically, thereby prompting clinicians to retake low-quality images when necessary. Yet, no user-friendly fidelity assessment tools currently address this need. Aim: Herein, we introduce a real-time, frame-by-frame FMI fidelity assessment method, termed no-reference fidelity assessment of fluorescence molecular imaging (NRFA-FMI). Approach: NRFA-FMI quantifies image fidelity using a combination of histogram-based and textural metrics. A central advantage of NRFA-FMI is that it operates without reference images, making it particularly suitable for clinical FMI workflows. Results: We show that NRFA-FMI outperforms two state-of-the-art no-reference image fidelity assessment methods, the blind/referenceless image spatial quality evaluator (BRISQUE) and the natural image quality evaluator (NIQE). Conclusions: The introduction of NRFA-FMI enables real-time FMI fidelity assessment, an essential step toward reducing data misinterpretation and preventing incorrect diagnosis. This approach provides clinicians with an intuitive, automated indicator of image reliability, supporting more consistent and accurate intraoperative decision-making.

Indexed as

Image Processing, Computer-AssistedMolecular ImagingOptical ImagingAlgorithmsHumansMicroscopy, Fluorescencefluorescence molecular imagingno-reference image fidelity assessmentquality controlstandardization

Identifiers

PMID42254326
PMCPMC13237650

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.