Catheter-Based Polarization Sensitive Optical Coherence Tomography Using Similar Mueller Matrix Method
Bibliographic record
Abstract
OBJECTIVE: Research of catheter-based polarization sensitive optical coherence tomography (PS-OCT) is a challenging field. In this paper, we present a new polarization determination method, similar Mueller matrix (SMM) method, for a catheter-based PS-OCT system using a standard clinical catheter probe with an outer diameter of 0.9 mm. METHODS: The SMM method can remove the diattenuation and depolarization compositions by polar decomposition. By constructing the similarity between the measured Mueller matrices and sample matrices, the phase retardance of the sample can be determined from the trace of the measured matrices. RESULTS: In the experiments, we find that images processed by the SMM method without any averaging or phase correction have a better polarization contrast of multiple biological tissues than those by the Jones matrix based method. We also preliminarily achieve phase retardance imaging of the ex vivo porcine cardiac blood vessel. CONCLUSION: Compared with the Jones matrix based method, the presented SMM method can provide a more robust birefringence imaging of biological tissues under low signal-to-noise ratio, depolarization, diattenuation, and phase instability. SIGNIFICANCE: The SMM method has a potential to become a widely accepted polarization determination method for catheter-based PS-OCT.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".