Evidence map›Paper›PMID 40008291›Full record

ArticleJournal of biomedical optics2025

Multi-spectral laser speckle contrast imaging for depth-resolved blood perfusion assessment.

Liban Hussein, Sajjad Moazeni

Abstract read
In one paragraph

Article in Journal of biomedical optics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
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

2 authors.

Liban HusseinUniversity of Washington, Department of Electrical and Computer Engineering, Seattle, Washington, United States.ORCID 0000-0002-2298-5424
Sajjad MoazeniUniversity of Washington, Department of Electrical and Computer Engineering, Seattle, Washington, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Significance: Laser speckle contrast imaging (LSCI) is a widely used tool in biomedical imaging that leverages the interactions between coherent laser light and tissue to assess blood perfusion. Although effective for 2D imaging applications such as skin burn assessment and wound healing, conventional LSCI lacks depth-resolved capabilities, limiting its potential for deeper perfusion analysis. Enhancing LSCI for depth profiling would significantly expand its utility in applications such as vascular imaging and burn diagnostics. Aim: We investigate the use of multi-spectral laser speckle contrast imaging (MS-LSCI) for assessing blood perfusion at multiple depths, utilizing multiple laser wavelengths and advanced correlation techniques to improve depth localization. Approach: Two tissue phantom molds were fabricated to simulate blood vessels at varying depths. Laser wavelengths from blue to near-infrared (NIR) were used to perform controlled experiments. The visibility parameter, Results: MS-LSCI demonstrated improved depth profiling accuracy across varying laser wavelengths. The spectral wavelength mapping technique enhanced signal quality for wavelengths with limited penetration. The visibility parameter, Conclusions: We highlight the potential of MS-LSCI for depth-resolved blood perfusion imaging using multi-wavelength approaches. The findings emphasize the technique's feasibility for non-invasive biomedical applications, including burn wound assessment and vascular imaging.

Indexed as

Laser Speckle Contrast ImagingPerfusion ImagingHandHumansImage Processing, Computer-AssistedPhantoms, ImagingSkindepth assessmentlaser speckle contrast imagingmultiple-wavelengthsmultispectralnon-invasive imagingskin burn characterization

Identifiers

PMID40008291
PMCPMC11853228

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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.