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Record W1994411235 · doi:10.1117/1.3290818

Real-time diffuse optical tomography based on structured illumination

2010· article· en· W1994411235 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

VenueJournal of Biomedical Optics · 2010
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsUniversité de MontréalPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsFrame rateDigital micromirror deviceDetectorDiffuse optical imagingTomographic reconstructionOpticsComputer scienceOptical tomographyTomographyFrame (networking)Iterative reconstructionDigital imagingResolution (logic)Materials scienceComputer visionArtificial intelligenceImage processingDigital imagePhysicsImage (mathematics)Telecommunications

Abstract

fetched live from OpenAlex

A new optical acquisition scheme based on a pair of digital micromirror devices is developed and applied to three-dimensional tomographic imaging of turbid media. By using pairs of illumination-detection patterns with a single detector, we were able to perform high-resolution quantitative volumetric imaging of absorption heterogeneities embedded in optically thick samples. Additionally, a tomographic reconstruction algorithm was implemented on a graphical processor unit to provide optical reconstructions at a frame rate of 2 Hz. The structured illumination method proposed in this work has significant cost advantages over camera systems, as only a single detector is required. This configuration also has the potential to increase frame rate.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.347
Threshold uncertainty score0.506

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.285
Teacher spread0.280 · 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