Lateral resolution enhancement via imbricated spectral domain optical coherence tomography in a maximum-a-posterior reconstruction framework
Bibliographic record
Abstract
The lateral resolution of a Spectral Domain Optical Coherence Tomography (SD-OCT) image is limited by the focusing properties of the OCT imaging probe optics, the wavelength range which SD-OCT system operates at, spherical and chromatic aberrations induced by the imaging optics, the optical properties of the imaged object, and in the special case of in-vivo retinal imaging by the optics of the eye. This limitation often results in challenges with resolving fine details and structures of the imaged sample outside of the Depth-Of-Focus (DOF) range. We propose a novel technique for generating Laterally Resolved OCT (LR-OCT) images using OCT measurements acquired with intentional imbrications. The proposed, novel method is based on a Maximum A Posteriori (MAP) reconstruction framework which takes advantage of a Stochastic Fully Connected Conditional Random Field (SFCRF) model to compensate for the artifacts and noise when reconstructing a LR-OCT image from imbricated OCT measurement. The proposed lateral resolution enhancement method was tested on synthetic OCT measurement as well as on a human cornea SDOCT image to evaluate the usefulness of the proposed approach in lateral resolution enhancement. Experimental results show that applying this method to OCT images, noticeably improves the sharpness of morphological features in the OCT image and in lateral direction, thus demonstrating better delineation of fine dot shape details in the synthetic OCT test, as well as better delineation of the keratocyte cells in the human corneal OCT test image.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".