Analytic expression of fluorescence ratio detection correlates with depth in multi-spectral sub-surface imaging
Bibliographic record
Abstract
Here we derived analytical solutions to diffuse light transport in biological tissue based on spectral deformation of diffused near-infrared measurements. These solutions provide a closed-form mathematical expression which predicts that the depth of a fluorescent molecule distribution is linearly related to the logarithm of the ratio of fluorescence at two different wavelengths. The slope and intercept values of the equation depend on the intrinsic values of absorption and reduced scattering of tissue. This linear behavior occurs if the following two conditions are satisfied: the depth is beyond a few millimeters and the tissue is relatively homogeneous. We present experimental measurements acquired with a broad-beam non-contact multi-spectral fluorescence imaging system using a hemoglobin-containing diffusive phantom. Preliminary results confirm that a significant correlation exists between the predicted depth of a distribution of protoporphyrin IX molecules and the measured ratio of fluorescence at two different wavelengths. These results suggest that depth assessment of fluorescence contrast can be achieved in fluorescence-guided surgery to allow improved intra-operative delineation of tumor margins.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".