Evidence map›Paper›PMID 41002355›Full record

ArticleBiosensors2025

Comparative Evaluation of Combined Denoising and Resolution Enhancement Algorithms for Intravital Two-Photon Imaging of Organs.

Saeed Bohlooli Darian, Woo June Choi, Jeongmin Oh, Jun Ki Kim

Abstract readComparative Study
In one paragraph

Article in Biosensors, 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

4 authors.

Saeed Bohlooli DarianDepartment of Biomedical Engineering, College of Medicine, University of Ulsan, Seoul 05505, Republic of Korea.
Woo June ChoiSchool of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea.ORCID 0000-0003-0793-2735
Jeongmin OhConvergence Medicine, Asan Institute for Life Sciences, Asan Medical Center, Seoul 05505, Republic of Korea.
Jun Ki KimDepartment of Biomedical Engineering, College of Medicine, University of Ulsan, Seoul 05505, Republic of Korea.ORCID 0000-0002-0099-9681

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Intravital two-photon microscopy enables deep-tissue imaging of subcellular structures in live animals, but its original spatial resolution and image quality are limited by scattering, motion, and low signal-to-noise ratios. To address these challenges, we used a combination of tissue stabilization, denoising methods, and motion correction, together with resolution enhancement algorithms, including enhanced Super-Resolution Radial Fluctuations (eSRRF) and deconvolution, to acquire high-fidelity time-lapse images of internal organs. We applied this imaging pipeline to image genetically labeled mitochondria in vivo, in Dendra2 mice. Our results demonstrate that the eSRRF-combined method, compared to other evaluated algorithms, significantly shows improved spatial resolution and mitochondrial structure visualization, while each method exhibiting distinct strengths in terms of noise tolerance, edge preservation, and computational efficiency. These findings provide a practical framework for selecting enhancement strategies in intravital imaging studies targeting dynamic subcellular processes.

Indexed as

AlgorithmsIntravital MicroscopyMicroscopy, Fluorescence, MultiphotonAnimalsImage Processing, Computer-AssistedMiceMitochondriaSignal-To-Noise Ratiocomputational enhancementdenoisingeSRRFmitochondriasuper-resolution imagingtwo-photon intravital microscopy

Identifiers

PMID41002355
PMCPMC12467442

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 Socratic graph.