Fiber-optic probe design and optical property recovery algorithm for optical biopsy of brain tissue
Bibliographic record
Abstract
Optical biopsy techniques offer a minimally invasive, real-time alternative to traditional biopsy and pathology during tumor resection surgery. Diffuse reflectance spectroscopy (DRS) is a commonly used technique in optical biopsy. Optical property recovery from spatially resolved DRS data allows quantification of the scattering and absorption properties of tissue. Monte Carlo simulation methods were used to evaluate a unique fiber-optic probe design for a DRS instrument to be used specifically for optical biopsy of the brain. The probe diameter was kept to a minimum to allow usage in small surgical cavities at least 1 cm in diameter. Simulations showed that the close proximity of fibers to the edge of the probe resulted in boundary effects due to reflection of photons from the surrounding air-tissue interface. A new algorithm for rapid optical property recovery was developed that accounts for this reflection and therefore overcomes these effects. The parameters of the algorithm were adjusted for use over the wide range of optical properties encountered in brain tissue, and its precision was evaluated by subjecting it to random noise. This algorithm can be adapted to work with any probe geometry to allow optical property recovery in small surgical cavities.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".