OCT-based approach to local relaxations discrimination from translational relaxation motions
Bibliographic record
Abstract
Multimodal optical coherence tomography (OCT) is an emerging tool for tissue state characterization. Optical coherence elastography (OCE) is an approach to mapping mechanical properties of tissue based on OCT. One of challenging problems in OCE is elimination of the influence of residual local tissue relaxation that complicates obtaining information on elastic properties of the tissue. Alternatively, parameters of local relaxation itself can be used as an additional informative characteristic for distinguishing the tissue in normal and pathological states over the OCT image area. Here we briefly present an OCT-based approach to evaluation of local relaxation processes in the tissue bulk after sudden unloading of its initial pre-compression. For extracting the local relaxation rate we evaluate temporal dependence of local strains that are mapped using our recently developed hybrid phase resolved/displacement-tracking (HPRDT) approach. This approach allows one to subtract the contribution of global displacements of scatterers in OCT scans and separate the temporal evolution of local strains. Using a sample excised from of a coronary arteria, we demonstrate that the observed relaxation of local strains can be reasonably fitted by an exponential law, which opens the possibility to characterize the tissue by a single relaxation time. The estimated local relaxation times are assumed to be related to local biologically-relevant processes inside the tissue, such as diffusion, leaking/draining of the fluids, local folding/unfolding of the fibers, etc. In general, studies of evolution of such features can provide new metrics for biologically-relevant changes in tissue, e.g., in the problems of treatment monitoring.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".