Real-time en-face Gabor optical coherence tomographic angiography on human skin using CUDA GPU
Bibliographic record
Abstract
We recently proposed an optical coherence tomographic angiography (OCTA) algorithm, Gabor optical coherence tomographic angiography (GOCTA), which can extract microvascular signals from a spectral domain directly with lower computational complexity compared to other algorithms. In this manuscript, we combine a programmable swept source, an OCT complex signal detecting unit, and graphics process units (GPU) to achieve a real-time en-face GOCTA system for human skin microvascular imaging. The programmable swept source can balance the A-scan rate and the spectral tuning range; the polarization-modulation based complex signal detecting unit can double the imaging depth range, and the GPU can accelerate data processing. C++ and CUDA are used as the programming platform where five parallel threads are created for galvo-driving signal generation, data acquisition, data transfer, data processing, and image display, respectively. Two queues (for the raw data and en-face images, respectively) are used to improve the data exchange efficiency among different devices. In this study, the data acquisition time and data processing time for each 3D complex volume (256×304×608 pixels,) are 405.3 and 173.7 milliseconds respectively. To the best of our knowledge, this is the first time to show en-face microvascular images covering 3×3 mm2 at a refresh rate of 2.5 Hz.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".