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Record W2076862124 · doi:10.1117/12.807669

Time-domain 3D localization of fluorescent inclusions in a thick scattering medium

2008· article· en· W2076862124 on OpenAlexaff
Julien Pichette, Éric Lapointe, Yves Bérubé-Lauzière

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2008
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsUniversité de Sherbrooke
FundersFlorida Department of Transportation
KeywordsFluorescenceOpticsLaserMaterials scienceImaging phantomScatteringPhysicsPhotonAbsorption (acoustics)DetectorPhoton countingTime domainComputer science

Abstract

fetched live from OpenAlex

We introduce an improved approach in the 3D localization of discrete fluorescent inclusions in a thick scattering medium. Previously our approach provided accurate localization of a single inclusion, showing the potential for direct time-of-flight fluorescence diffuse optical tomography. Here, we localize various combinations of multiple fluorescent inclusions. We resort to time-domain (TD) detection of emitted fluorescence pulses after short pulse laser excitation. Our approach relies on a signal processing technique, dubbed numerical constant fraction discrimination (NCFD), for extracting in a stable manner the arrival time of early photons emitted by one or many fluorescent inclusions from measured time-of-flight (TOF) distributions. Our experimental set-up allows multi-view tomographic optical TD measurements over 360 degrees without contact with the medium. It uses an ultra-short pulse laser and ultra-fast time-correlated single photon counting (TCSPC) detection. Fluorescence time point-spread functions (FTPSFs) are acquired all around the phantom after laser excitation. From measured FTPSFs, the arrival time of a fluorescent wavefront at a detector position is extracted with our NCFD technique. Indocyanine green (ICG; absorption peak = 780nm, emission peak = 830nm) is used for the inclusions. Various experiments were conducted with this set-up in a stepwise fashion. First, single inclusion experiments are presented to provide background information. Second, we present results using two inclusions in a plane. Then, we move on with two inclusions located in different planes. Finally, we show results with a plurality of inclusions (>2) distributed at arbitrary positions in the medium. Using an algorithm we have developed and tested on the acquired data, we successfully achieve to locate the inclusions. Here, results are obtained for discrete inclusions. In a close future, we expect to extend our method to continuous fluorescence distributions.

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)0.000
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: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.010
GPT teacher head0.252
Teacher spread0.242 · 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

Citations13
Published2008
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicOptical Imaging and Spectroscopy TechniquesFrench-language works237,207