Advanced multi-contrast Jones matrix optical coherence tomography for Doppler and polarization sensitive imaging
Bibliographic record
Abstract
An advanced version of Jones matrix optical coherence tomography (JMT) is demonstrated for Doppler and polarization sensitive imaging of the posterior eye. JMT is capable of providing localized flow tomography by Doppler detection and investigating the birefringence property of tissue through a three-dimensional (3-D) Jones matrix measurement. Owing to an incident polarization multiplexing scheme based on passive optical components, this system is stable, safe in a clinical environment, and cost effective. Since the properties of this version of JMT provide intrinsic compensation for system imperfection, the system is easy to calibrate. Compared with the previous version of JMT, this advanced JMT achieves a sufficiently long depth measurement range for clinical cases of posterior eye disease. Furthermore, a fine spectral shift compensation method based on the cross-correlation of calibration signals was devised for stabilizing the phase of OCT, which enables a high sensitivity Doppler OCT measurement. In addition, a new theory of JMT which integrates the Jones matrix measurement, Doppler measurement, and scattering measurement is presented. This theory enables a sensitivity-enhanced scattering OCT and high-sensitivity Doppler OCT. These new features enable the application of this system to clinical cases. A healthy subject and a geographic atrophy patient were measured in vivo, and simultaneous imaging of choroidal vasculature and birefringence structures are demonstrated.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".