Evidence map›Paper›PMID 42603887›Full record

ArticlePhotoniX2026

Pixel super-resolved fluorescence lifetime imaging using deep neural networks.

Paloma Casteleiro Costa, Parnian Ghapandar Kashani, Xuhui Liu, Alexander Chen, Ary Portes, Julien Bec, Laura Marcu, Aydogan Ozcan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PhotoniX, 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

8 authors.

Paloma Casteleiro CostaElectrical and Computer Engineering Department, University of California, Los Angeles, CA 90095 USA.
Parnian Ghapandar KashaniElectrical and Computer Engineering Department, University of California, Los Angeles, CA 90095 USA.
Xuhui LiuDepartment of Biomedical Engineering, University of California, Davis, CA 95616 USA.
Alexander ChenElectrical and Computer Engineering Department, University of California, Los Angeles, CA 90095 USA.
Ary PortesElectrical and Computer Engineering Department, University of California, Los Angeles, CA 90095 USA.
Julien BecDepartment of Biomedical Engineering, University of California, Davis, CA 95616 USA.
Laura MarcuDepartment of Biomedical Engineering, University of California, Davis, CA 95616 USA.
Aydogan OzcanElectrical and Computer Engineering Department, University of California, Los Angeles, CA 90095 USA.ORCID 0000-0002-0717-683X

Funding

TRD3: Data Analytics and Intelligent Systems (AI-ML-DL-Visualization)P41EB032840 · NIBIB · UNIVERSITY OF CALIFORNIA AT DAVIS · PI Griffith R. Harsh, Laura Marcu · 2022 to 2026
$6.7M
NIBIB NIH HHS P41 EB032840
6 · The paper itself

Abstract

Fluorescence lifetime imaging microscopy (FLIM) is a powerful quantitative technique that provides metabolic and molecular contrast, offering strong translational potential for label-free, real-time diagnostics. However, its clinical adoption remains limited by long pixel dwell times and low signal-to-noise ratio (SNR), which impose a more severe resolution-speed trade-off than conventional optical imaging approaches. Here, we introduce FLIM Supplementary Information: The online version contains supplementary material available at 10.1186/s43074-026-00277-9.

Indexed as

Computational microscopyConditional generative adversarial networks (cGAN)Deep learningFluorescence lifetime imaging microscopy (FLIM)Pixel super-resolution

Identifiers

PMID42603887
PMCPMC13476302

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.