Optical-resolution photoacoustic micro-endoscopy with ultrasound array system detection
Bibliographic record
Abstract
Recently we demonstrated the feasibility of Optical-Resolution Photoacoustic Micro-Endoscopy (OR-PAME) using an image guide fiber. However, the use of an ultrasound transducer for signal collection limited useful applications. We demonstrate detection of OR-PAM signals using an external array transducer in order to make endoscopic imaging practical for clinical use for the first time. The array system is able to visualize the placement of the image-guide fiber using pulse-echo ultrasound then switch to an OR-PAME acquisition mode. Photoacoustic signals are captured by a Verasonics ultrasound system using an L7-4 linear array transducer. A high-repetition-rate 532-nm fiber laser was used as the excitation source. This light was focused and raster scanned into a 800m-diameter image-guide fiber bundle consisting of 30,000 individual fiber elements. The operator finds the end of the endoscope using a flash ultrasound imaging mode, then captures endoscopic data by clicking a button. This activates the motion of scanning mirrors into the end of the image guide, and engages an endoscopic capture sequence. Endoscopic data are used to form a maximum amplitude image by simply taking the maximum of the absolute value of the signal across the 64 center channel lines used for capture. Using this technique, we have captured images of carbon fibers with a resolution of 6 microns at an SNR of greater than 30dB. Electronic focusing is expected to improve the SNR. The use of an ultrasound array transducer for both endoscope guidance and data collection allows for a much smaller endoscope footprint while opening up clinical possibilities.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".