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Record W4409468666 · doi:10.1016/j.isci.2025.112433

Laser speckle size and contrast investigation of volumetric scattering from controlled turbid phantoms and mouse skin tissues

2025· article· en· W4409468666 on OpenAlexafffund
Daniel C. Louie, I. Alex Vitkin

Bibliographic record

VenueiScience · 2025
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health NetworkUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchUniversity of Toronto
KeywordsSpeckle patternLaserContrast (vision)OpticsMaterials scienceSpeckle imagingLight scatteringScatteringBiomedical engineeringPhysicsMedicine

Abstract

fetched live from OpenAlex

The potential of spatial laser speckle analysis to interrogate scattering properties of volumetric optical tissue-like phantoms and biological tissues is examined. The simple and inexpensive experimental setup consists of a HeNe laser illuminating turbid samples to acquire speckle patterns in backscattering geometry. Theoretical Monte Carlo simulations of subsurface light fluence patterns and scattering statistics are used to interpret the experimental findings, in an effort to help expand established speckle theory from rough surface effects to volumetric scattering processes. Beyond controlled phantom studies, initial results from normal and pathologic biological tissues are also presented and discussed. Building on previous research that attempts to link specific stochastic speckle metrics to medium scattering properties, this study suggests that a combination of speckle contrast and speckle size can be related to varying scattering coefficients, toward distinguishing samples by their underlying scatterer size and concentration.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.239

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.284
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2025
Admission routes2
Has abstractyes

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