MétaCan
Menu
Back to cohort
Record W1980972185 · doi:10.1364/ao.49.003566

Quantitative photoacoustic tomography with multiple optical sources

2010· article· en· W1980972185 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.

Bibliographic record

VenueApplied Optics · 2010
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsOpticsPhotoacoustic imaging in biomedicinePhotoacoustic tomographyMaterials sciencePhotoacoustic effectAbsorption (acoustics)Attenuation coefficientDiffuse optical imagingImage resolutionPhotoacoustic spectroscopyOptical tomographyOptical imagingResolution (logic)TomographyComputer sciencePhysics

Abstract

fetched live from OpenAlex

Quantitative imaging of optical properties of biological tissues with high resolution has been a long-sought-after goal of many research groups. Photoacoustic imaging is a hybrid bio-optical imaging technique offering optical absorption contrast with ultrasonic spatial resolution. While photoacoustic methods offer significant promise for high-resolution optical imaging, quantification has thus far proved challenging. In this paper, a noniterative reconstruction technique for producing quantitative photoacoustic images of absorption perturbations is introduced for the case when the optical properties of the turbid background are known and when multiple optical illumination locations are used. Through theoretical developments and computational examples it is demonstrated that multiple-optical-source photoacoustic imaging can produce quantitative optical absorption reconstructions. The combination of optical and photoacoustic measurements is shown to yield improved reconstruction stability.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.771
Threshold uncertainty score0.815

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 integrity0.0000.001
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 head0.005
GPT teacher head0.190
Teacher spread0.185 · 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