Time-domain geometrical localization of point-like fluorescence inclusions in turbid media with early photon arrival times
Bibliographic record
Abstract
We introduce a novel approach for localizing a plurality of discrete point-like fluorescent inclusions embedded in a thick turbid medium using time-domain measurements. The approach uses early photon information contained in measured time-of-flight distributions originating from fluorescence emission. Fluorescence time point-spread functions (FTPSFs) are acquired with ultrafast time-correlated single photon counting after short pulse laser excitation. Early photon arrival times are extracted from the FTPSFs obtained from several source-detector positions. Each source-detector measurement allows defining a geometrical locus where an inclusion is to be found. These loci take the form of ovals in 2D or ovoids in 3D. From these loci a map can be built, with the maxima thereof corresponding to positions of inclusions. This geometrical approach is supported by Monte Carlo simulations performed for biological tissue-like media with embedded fluorescent inclusions. To validate the approach, several experiments are conducted with a homogeneous phantom mimicking tissue optical properties. In the experiments, inclusions filled with indocyanine green are embedded in the phantom and the fluorescence response to a short pulse of excitation laser is recorded. With our approach, several inclusions can be localized with low millimeter positional error. Our results support the approach as an accurate, efficient, and fast method for localizing fluorescent inclusions embedded in highly turbid media mimicking biological tissues. Further Monte Carlo simulations on a realistic mouse model show the feasibility of the technique for small animal imaging.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".