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Record W2072245109 · doi:10.1063/1.3116132

Interstitial point radiance spectroscopy of turbid media

2009· article· en· W2072245109 on OpenAlexafffund
Lee Chin, Brendan Lloyd, William M. Whelan, I. Alex Vitkin

Bibliographic record

VenueJournal of Applied Physics · 2009
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsUniversity Health NetworkToronto Metropolitan UniversityUniversity of Prince Edward IslandUniversity of Toronto
FundersNational Cancer InstituteNatural Sciences and Engineering Research Council of Canada
KeywordsRadianceOpticsScatteringPoint sourceLight scatteringSpectroscopyPhysicsAttenuation coefficientImaging spectroscopyMaterials scienceRemote sensingGeology

Abstract

fetched live from OpenAlex

We present an optical technique, point radiance spectroscopy, to directly recover chromophore concentrations and the reduced optical scattering coefficient spectrum from continuous wave interstitial point radiance measurements at a single-source-detector separation in turbid, tissuelike media. The method employs a spectral algorithm to fit the relative radiance data, using the P3 approximation, at only two detection angles (0° and 90°). The spectral fitting algorithm is applied to simulated data of relative point fluence and relative point radiance data with added 1% noise and shows that even under realistic experimental conditions, only point radiance information is able to provide quantitative information regarding chromophore concentrations and scattering power at distances greater than two to three mean free paths from the source. Furthermore, experimental measurements in tissue-simulating phantoms demonstrate that dye concentrations and scattering parameters can be recovered to within ∼10%. The developed point radiance technique bridges a technological gap between local surface reflectance and spatially resolved interstitial fluence methods in optical assessment of random media such as biological tissue.

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 score0.089
Threshold uncertainty score0.383

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 head0.010
GPT teacher head0.288
Teacher spread0.278 · 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

Citations13
Published2009
Admission routes2
Has abstractyes

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