Snapshot Imaging of Stokes Vector Polarization Speckle in Turbid Optical Phantoms and In Vivo Tissues
Bibliographic record
Abstract
Significance: We present a system to measure and analyze the complete polarization state distribution of speckle patterns generated from in vivo tissue. Accurate measurement of polarization speckle requires both precise spatial registration and rapid polarization state acquisition. A unique measurement system must be designed to achieve accurate images of polarization speckle patterns for detailed investigation of the scattering properties of biological tissues in vivo. Aim and approach: This system features a polarization state analyzer with no moving parts. Two pixel-polarizer cameras allow for the instantaneous acquisition of the spatial Stokes vector distribution of polarization speckle patterns. System design and calibration methods are presented, and representative images from measurements on liquid phantoms (microsphere suspensions) and in vivo healthy and tumor murine models are demonstrated and discussed. Results and Conclusions: Quantitative measurements of polarization speckle from microsphere suspensions with controlled scattering coefficients demonstrate differences in speckle contrast, speckle size, and the degree of polarization. Measurements on in vivo murine skin and xenograft tumor tissue demonstrate the ability of the system to acquire snapshot polarization speckle images in living systems. The developed system can thus rapidly and accurately acquire polarization speckle images from different media in dynamic conditions such as in vivo tissue. This capability opens the potential for future detailed investigation of polarization speckle for in vivo biomedical applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".