Mueller matrix decomposition for extraction of individual polarization parameters from complex turbid media exhibiting multiple scattering, optical activity, and linear birefringence
Bibliographic record
Abstract
Linear birefringence and optical activity are two common optical polarization effects present in biological tissue, and determination of these properties has useful biomedical applications. However, measurement and unique interpretation of these parameters in tissue is hindered by strong multiple scattering effects and by the fact that these and other polarization effects are often present simultaneously. We have investigated the efficacy of a Mueller matrix decomposition methodology to extract the individual intrinsic polarimetry characteristics (linear retardance delta and optical rotation psi, in particular) from a multiply scattering medium exhibiting simultaneous linear birefringence and optical activity. In the experimental studies, a photoelastic modulation polarimeter was used to record Mueller matrices from polyacrylamide phantoms having strain-induced birefringence, sucrose-induced optical activity, and polystyrene microspheres-induced scattering. Decomposition of the Mueller matrices recorded in the forward detection geometry from these phantoms with controlled polarization properties yielded reasonable estimates for delta and psi parameters. The confounding effects of scattering, the propagation path of multiple scattered photons, and detection geometry on the estimated values for delta and psi were further investigated using polarization-sensitive Monte Carlo simulations. The results show that in the forward detection geometry, the effects of scattering induced linear retardance and diattenuation are weak, and the decomposition of the Mueller matrix can retrieve the intrinsic values for delta and psi with reasonable accuracy. The ability of this approach to extract the individual intrinsic polarimetry characteristics should prove valuable in diagnostic photomedicine, for example, in quantifying the small optical rotations due to the presence of glucose in tissue and for monitoring changes in tissue birefringence as a signature of tissue abnormality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".