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Record W4252058665 · doi:10.32920/ryerson.14648115.v1

Decorrelation Time Analysis Using Dynamic Light Scattering with Optical Coherence Tomography in an in vivo Mouse Tumour Model

2021· preprint· en· W4252058665 on OpenAlexaff
Timothy Wan Hei Luk

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsToronto Metropolitan UniversityUniversity of Toronto
Fundersnot available
KeywordsOptical coherence tomographyDecorrelationSpeckle patternIn vivoRepeatabilityBiomedical engineeringInterferometryOpticsCoherence (philosophical gambling strategy)Optical tomographyPreclinical imagingPixelPhysicsNuclear medicineMaterials scienceComputer scienceChemistryMedicineComputer visionBiology

Abstract

fetched live from OpenAlex

Optical coherence tomography (OCT) is an imaging modality that uses near infrared light interferometry for non-invasive, near-histological resolution imaging at the micron level. Concepts from dynamic light scattering (DLS) can be adapted to OCT to detect and measure the motions in the target tissue. Tissue dynamics can be observed by measuring the speckle decorrelation time (DT) of the tissue. DT analysis was performed in a preclinical study to demonstrate the repeatability and feasibility of using DLS-OCT to observe mouse tumours undergoing cisplatin treatment over a 48-hour period. Differences in the average DT data were observed for control and cisplatin-injected mice. Image segmentation based on DT values was also performed to subtract the DT contributions of pixels at blood vessel locations, resulting in the improvement of average DT calculations of the tumour tissue. The results presented are a preliminary step to analyzing and monitoring tumour growth and treatment response in vivo.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.243
Teacher spread0.232 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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