Three-dimensional opto-thermo-mechanical model for predicting photo-thermal optical coherence tomography responses in multilayer geometries
Bibliographic record
Abstract
Photothermal optical coherence tomography (PT-OCT) is a functional extension of OCT with the ability to generate qualitative maps of molecular absorptions co-registered with the micron-resolution structural tomograms of OCT. Obtaining refined insight into chemical information from PT-OCT images, however, requires solid understanding of the multifactorial physics behind generation of PT-OCT signals and their dependence on system and sample parameters. Such understanding is needed to decouple the various physical effects involved in the PT-OCT signal to obtain more accurate insight into sample composition. In this work, we propose an analytical model that considers the opto-thermo-mechanical properties of multi-layered samples in 3-D space, eliminating several assumptions that have been limiting previous PT-OCT models. In parametric studies, the model results are compared with experimental signals to investigate the effect of sample and system parameters on the acquired signals. The proposed model and the presented findings open the door for: 1) better understanding of the effects of system parameters and tissue opto-thermo-mechanical properties on experimental signals; 2) informed optimization of experimentation strategies based on sample and system parameters; 3) guidance of downstream signal processing for predicting tissue molecular composition.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".