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Record W4230900680 · doi:10.32920/ryerson.14656545

Non-Invasive Measurement Of Tissue Chromophore Concentrations And Cerebral Blood Flow Using Broad-Band Continuous Wave Near Infrared Spectroscopy

2021· preprint· en· W4230900680 on OpenAlexafffund
Hadi Zabihi-Yeganeh

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsToronto Metropolitan University
FundersHeart and Stroke Foundation of Canada
KeywordsIndocyanine greenMultispectral imageChromophoreMaterials scienceNear-infrared spectroscopySpectroscopyOpticsInfraredWavelengthBlood flowDiffuse optical imagingBiomedical engineeringDiffuse reflectance infrared fourier transformAbsorption (acoustics)Analytical Chemistry (journal)ChemistryOptoelectronicsRemote sensingPhysicsMedicineChromatographyInternal medicine

Abstract

fetched live from OpenAlex

We present a broad-band, continuous wave spectral approach to quantify the baseline optical properties of tissue, in particular the absolute absorption and scattering properties and changes in the concentrations of chromophores, which can assist to quantify the regional blood flow from dynamic contrast-enhanced near-infrared spectroscopy data. Experiments were conducted on phantoms and piglets. The baseline optical properties of tissue were determined by performing a multi-parameter wavelength-dependent differential data fit of the near infrared reflectance spectrum between 680 nm and 970 nm of a photon diffusion equation solution for a semi-infinite homogeneous medium. These baseline optical properties of the piglet head tissue were used to quantify the temporal dynamics of the concentration of the intravenously administered contrast agent Indocyanine Green in the piglet brain. The temporal traces of the Indocyanine Green concentration measured by our method were used to estimate the cerebral blood flow using a bolus tracking technique.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.000
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.026
GPT teacher head0.293
Teacher spread0.267 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2021
Admission routes2
Has abstractyes

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