Three-dimensional localization of discrete fluorescent inclusions from multiple tomographic projections in the time-domain
Bibliographic record
Abstract
We introduce a novel approach for localizing a plurality of discrete fluorescent inclusions embedded in a thick scattering medium using time-domain (TD) experimental data. It relies on numerical constant fraction discrimination (NCFD), a signal processing technique for extracting in a stable manner the arrival time of early photons emitted by one or many fluorescent inclusions from measured photons time of flight (TOF) distributions. Our experimental set-up allows multi-view TD data acquisition from multiple tomographic projections over 360 degrees without contact with the medium. Fluorescence time point-spread functions (FTPSFs) are acquired all around the medium with ultra-fast time-correlated single photon counting (TCSPC) after short pulse laser excitation. From these FTPSFs, the early photons arrival time (EPAT) of a fluorescent wavefront at a detector position is extracted with our NCFD technique. The key to our localization algorithm is to combine EPATs from several detection positions and projections to form 3D surfaces. The digital analysis of the concavities of the surfaces allows to find the 3D positions of an a priori unknown number of fluorescent inclusions located in the medium. Indocyanine green (ICG; absorption peak = 780nm, emission peak = 830nm) is used for the inclusions. Various experiments were conducted, and we show localization results on experimental data for up to 5 discrete inclusions distributed at arbitrary positions in the medium. We expect to extend our method to continuous distributions of fluorescence (rather than discrete inclusions) in a near future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".