Experimental method to assess depth sensing limits of inelastic scattering measurements using spatial-offset Raman spectroscopy imaging
Bibliographic record
Abstract
SignificanceThe relationship between spatial offset and tissue sensing depth is not well understood in spatial offset Raman spectroscopy (SORS). Detection of the subsurface biochemical composition could improve clinical translation of SORS-based methods, including for lumpectomy margin characterization in breast cancer surgery.AimWe aimed at developing an experimental method to establish a relationship between spatial offset in SORS and sampling depth. The technique was developed using a custom hyperspectral line-scanning imaging system optimized for Raman spectroscopy detection.ApproachBilayer phantoms were produced with top and bottom layers made of material with different Raman spectroscopy signatures, i.e., poly(dimethylsiloxane) polymer (PDMS) and Nylon. The top layer of PDMS had different values of absorption and reduced elastic scattering coefficients, as well as a thickness up to ∼3 mm. A metric was used, called spectral angle mapper, that allowed for comparing SORS measurements with reference spectra of pure PDMS and Nylon. That metric was used to develop a technique predicting sensing depth for different values of spatial offset. A proof-of-concept study was performed to assess the performance of the method in biological tissue, demonstrating detectability of protein-rich tissue across layers of Intralipid and porcine fat to simulate the optical properties of human adipose tissue.ResultsA total of 60 optical phantoms with varying optical properties and top layer thicknesses were imaged and processed to estimate sampling depth as a function of spatial offset. The study demonstrated the detectability of the underlying Nylon layer across a PDMS layer up to 3 mm in thickness. Similarly, the detectability of protein-rich tissue was demonstrated across layers of Intralipid up to 3 mm thick and <2 mm for porcine fat.ConclusionsWe showed the feasibility of using bilayer solid optical phantoms to create correlation curves between the optimal spatial offset for a desired probed depth given the optical properties of the top layer. The technique could facilitate the clinical translation of SORS measurements for tumor detection and margins assessment.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".