High-sensitivity detection and monitoring of microcirculation using cutaneous and catheter probes for Doppler optical coherence tomography
Bibliographic record
Abstract
Background: Currently clinical Doppler ultrasound cannot detect microvascular blood flow and it is difficult to provide depth discrimination using laser Doppler flowmetry. Doppler optical coherence tomography (DOCT) is a novel technique for noninvasive subsurface imaging of microcirculation and tissue structure. Aims: To design handheld and catheter-based DOCT probes for clinical cutaneous and endoscopic imaging. To develop signal processing techniques for real-time detection and quantification of microvascular blood flow. Methods: A DOCT system, with interchangeable cutaneous and catheter probes, was developed. The axial spatial resolution was 10 μm, and the velocity resolution was 20 μm/s, using a 1300 nm broadband infrared light. The system achieved real-time imaging with frame rates up to 32 Hz at 512 x 256 pixels per frame. We used the system to detect microcirculation in human skin and rat esophagus, and to monitor microvascular responses to photodynamic therapy (PDT) in a rat tumor model. Results: We present experimental results from in vivo DOCT imaging of microcirculation in human skin arterio-venous malformations (AVM), normal rat esophagus, and a rat gliosarcoma PDT model. In the PDT model, we followed microvascular responses to PDT and observed differences in the microcirculation during and after therapy, which can have important implications for PDT dosimetry and treatment optimization. Conclusions: To our knowledge, this is the first demonstration of endoscopic catheter-based DOCT detection of microcirculation in vivo. In addition, AVM can be detected using handheld cutaneous DOCT probes under clinical settings. DOCT may serve as a real-time monitoring tool for PDT dosimetry, especially for vascular targeting photosensitizers.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".