Optical coherence tomography: technology and applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it