System and methods for wide-field quantitative fluorescence imaging during neurosurgery
Bibliographic record
Abstract
We report an accurate, precise and sensitive method and system for quantitative fluorescence image-guided neurosurgery. With a low-noise, high-dynamic-range CMOS array, we perform rapid (integration times as low as 50 ms per wavelength) hyperspectral fluorescence and diffuse reflectance detection and apply a correction algorithm to compensate for the distorting effects of tissue absorption and scattering. Using this approach, we generated quantitative wide-field images of fluorescence in tissue-simulating phantoms for the fluorophore PpIX, having concentrations and optical absorption and scattering variations over clinically relevant ranges. The imaging system was tested in a rodent model of glioma, detecting quantitative levels down to 20 ng/ml. The resulting performance is a significant advance on existing wide-field quantitative imaging techniques, and provides performance comparable to a point-spectroscopy probe that has previously demonstrated significant potential for improved detection of malignant brain tumors during surgical resection.
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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".