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Record W2898672015 · doi:10.1016/j.pacs.2018.10.002

Wavelength optimization in the multispectral photoacoustic tomography of the lymphatic drainage in mice

2018· article· en· W2898672015 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

VenuePhotoacoustics · 2018
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsSt. Michael's HospitalUniversity of TorontoToronto Metropolitan University
FundersCanadian Institutes of Health ResearchRyerson University
KeywordsMultispectral imageWavelengthImage qualityMaterials scienceOpticsTomographyPhotoacoustic imaging in biomedicineBiomedical engineeringComputer scienceImage (mathematics)OptoelectronicsArtificial intelligencePhysicsMedicine

Abstract

fetched live from OpenAlex

Multispectral photoacoustic tomography provides mapping of the tissue chromophore distributions using sets of tunable laser wavelengths. With the overall goal of studying the dynamics of cerebrospinal fluid in mice in vivo, our work aims to minimize the number of wavelengths to reduce scanning time, improve the temporal resolution, reduce the energy deposition and avoid the tracer photobleaching while maintaining high image quality. To select small sets of wavelengths we directly searched for the combinations of wavelengths providing the best and worst image quality in comparison with a reference image obtained using 131 closely spaced wavelengths between 680 and 940 nm in terms of the peak signal-to-noise ratio (PSNR). We have shown that using the PSNR optimization method, additional improvements could be achieved over the wavelength set selected using the method of the minimization of the extinction matrix condition number.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.128
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.006
GPT teacher head0.203
Teacher spread0.197 · 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