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Record W1968974709 · doi:10.1364/ol.35.002121

Time-resolved diffuse optical tomography with patterned-light illumination and detection

2010· article· en· W1968974709 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueOptics Letters · 2010
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsPolytechnique Montréal
FundersNational Institute of Biomedical Imaging and BioengineeringNatural Sciences and Engineering Research Council of Canada
KeywordsDiffuse optical imagingOpticsMonte Carlo methodTransmittanceLight fieldTime domainExcitationPhoton diffusionOptical tomographyData setField (mathematics)TomographyPoint spread functionPhysicsComputer scienceArtificial intelligenceComputer visionLight sourceMathematics

Abstract

fetched live from OpenAlex

This investigation explores the feasibility of performing diffuse optical tomography based on time-domain wide-field illumination and detection strategies. Wide-field patterned excitation and detection schemes are investigated in transmittance geometry with time-gated detection channels. A Monte Carlo forward model is employed to compute the time-resolved Jacobians for rigorous light propagation modeling. We demonstrate both in silico and experimentally that reconstructions of absorption structures based on wide-field patterned-light strategies are feasible and outperform classical point excitation schemes for similar data set sizes. Moreover, we demonstrate that time-domain information is retained even though large spatial areas are illuminated. The enhanced time-domain data set allows for quantitative three-dimensional imaging in thick tissue based on relatively small data sets associated with much shorter acquisition times.

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.

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 score0.034
Threshold uncertainty score0.484

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 head0.003
GPT teacher head0.225
Teacher spread0.222 · 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