Time-of-flight non-contact fluorescence diffuse optical tomography with numerical constant fraction discrimination
Bibliographic record
Abstract
We introduce a novel non-contact fluorescence diffuse optical tomography (FDOT) approach for localizing a fluorescent inclusion embedded in a scattering medium. It uses the time of flight of early photons arriving at several detector positions around the medium. It is a true and direct time-of-flight approach in that arrival times are converted to distance. The arrival time of early photons is found via a recently introduced numerical constant fraction discriminator applied to fluoresced photons time-of-flight distributions (fluorescence time pointspread functions (FTPSFs)). Time-correlated single photon counting and an ultrafast photon counting avalanche photodiode are used for measuring FTPSFs that form tomographic data sets. The FDOT localization algorithm proceeds in two steps. The first determines the angular position of the inclusion as the average, over projections, of angular detector positions with smallest arrival time. The second determines the inclusion's radial position based on relative arrival times obtained at several detector positions within each tomographic projection relatively to a reference detector position, the latter being that of shortest arrival time in the projection. The radial position found minimizes the discrepancy between relative arrival times computed for several possible inclusion positions and relative arrival times deduced from experimental data. Two methods are presented for this.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".