Depth resolution and multiexponential lifetime analyses of reflectance-based time-domain fluorescence data
Bibliographic record
Abstract
Time-domain fluorescence imaging is a powerful new technique that adds a rich amount of information to conventional fluorescence imaging. Specifically, time-domain fluorescence can be used to remove autofluorescence from signals, resolve multiple fluorophore concentrations, provide information about tissue microenvironments, and, for reflectance-based imaging systems, resolve inclusion depth. The present study provides the theory behind an improved method of analyzing reflectance-based time-domain data that is capable of accurately recovering mixed concentration ratios of multiple fluorescent agents while also recovering the depth of the inclusion. The utility of the approach was demonstrated in a number of simulations and in tissuelike phantom experiments using a short source-detector separation system. The major findings of this study were (1) both depth of an inclusion and accurate ratios of two-fluorophore concentrations can be recovered accurately up to depths of approximately 1 cm with only the optical properties of the medium as prior knowledge, (2) resolving the depth and accounting for the dispersion effects on fluorescent lifetimes is crucial to the accuracy of recovered ratios, and (3) ratios of three-fluorophore concentrations can be resolved at depth but only if the lifetimes of the three fluorophores are used as prior knowledge. By accurately resolving the concentration ratios of two to three fluorophores, it may be possible to remove autofluorescence or carry out quantitative techniques, such as reference tracer kinetic modeling or ratiometric approaches, to determine receptor binding or microenvironment parameters in point-based time-domain fluorescence applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".