Quantitative fluorescence imaging enabled by spatial frequency domain optical-property mapping in the sub-diffusive regime for surgical guidance
Bibliographic record
Abstract
Intraoperative fluorescence guidance enables maximum safe resection of, for example, glioblastomas by providing surgeons with real-time tumor optical contrast. Specifically, 5-aminolevulinic acid (ALA)-induced protoporphyrin IX (PpIX) fluorescence guided resection can improve surgical outcomes by better defining tumor margins and identifying satellite tumor foci. However, visual assessment of PpIX fluorescence is subjective and limited by the distorting effects of light attenuation (absorption and scattering) by tissue and background tissue autofluorescence. We have previously shown, using a point fluorescence-reflectance fiberoptic probe, that non-invasive measurement of the absolute PpIX concentration, [PpIX], further improves sensitivity and specificity, leading to the demonstration that the technique can also detect low-grade gliomas as well as otherwise undetectable residual tumor foci in high-grade disease. Here, we extend this approach to wide-field quantitative fluorescence imaging (qFI) by implementing spatial frequency domain imaging (SFDI) to recover the tissue optical absorption and transport scattering coefficients across the field of view. We report on the performance of this approach to determine the intrinsic fluorescence intensity in tissue-simulating phantoms in both the fully diffusive (i.e. scatter-dominated) and sub-diffusive (low transport albedo) regimes, for which higher spatial frequencies are used. The performance of qFI is compared to a Born- normalization correction scheme, as well as to the values obtained using the fiberoptic probe on homogeneous tissue phantoms containing PpIX.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".