Optical coherence tomography: technology and applications
Bibliographic record
Abstract
Optical coherence tomography (OCT) has recently emerged as a powerful optical imaging instrument and technology. OCT performs high resolution, cross-sectional tomographic imaging of the internal structure in 3D materials including biological tissues. Advantages of OCT vs. other imaging systems are: 1) High resolution: enables greater visualization of defects. (OCT: 5-10 microns, ultrasound: 150 microns. High resolution CT: 300 microns. MRI: 1,000 microns). 2) Noninvasive, non-contact: increase ease of use. 3) Fiber-optics delivery: allows OCT to be used in catheters and endoscopes. (Fiber diameter is normally 125 microns). 4) High speed: enables high-resolution 3D imaging. 5) Potential for additional information: polarization contrast and spectroscopic information can be obtained concurrently yielding new information of the testing tissues. 6) Use of non-harmful radiation. In this paper, we shortly review the technologies of OCT and present our works in design and implementation of fiber based OCT systems and full-field OCT systems, including high performance swept source, fibre probe, hardware, software design as well as system configurations. The applications of OCT involving in medical imaging, industrial inspection, information storage and retrieval, as well as biometrics and document security are also briefly introduced and demonstrated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".