Live feedback and 3D photoacoustic remote sensing
Bibliographic record
Abstract
Background: As photoacoustic (PA) techniques progress towards clinical adoption, providing a high-speed live feedback becomes a high priority. To keep up with the instantaneous optical feedback of conventional light microscopes, PA imaging would need to provide a high-resolution video-rate live feed to the user. However, conventional PA microscopy typically trades resolution, sensitivity and imaging speed when optically scanning due to the difficult opto-acoustic confocal geometry. Here, we employ photoacoustic remote sensing (PARS), an all-optical technique that relies on optical confocal geometry, to provide a high-resolution live display in a reflection-mode PA architecture. Methods: Employing a conventional x-y galvanometer scanner and a 600 KHz pulse repetition rate laser we implement a system capable of acquiring 2.5 frames per second in 2D. To complement this fast scanning optical system, we implement a computationally inexpensive image reconstruction method that is able to render the frames with minimal overhead, providing a live display. Results: Employing the proposed method, we demonstrate a live feedback with frame rates as high as 2.5 Hz in 2D and also report the first results of 3D imaging with a non-contact label-free reflection-mode technique. The method is validated with phantom studies and in-vivo imaging. Employing a repetition rate of 600 KHz, a live feed of carbon fibers is realized with a C-scan rate of 2.5 Hz. The imaging resolution was measured to be 1.2 µm, the highest reported for a real-time reflection-mode architecture. The mean and peak SNR were measured to be 44 and 62 dB respectively in-vivo. 3D visualizations of carbon fiber phantoms and mouse ear microvasculature structure are also demonstrated. Conclusions: In summary, we present a method that has a small computational overhead for image rendering, resulting in a live display capable of real-time frame rates. We also report the first 3D imaging with a non-contact label-free reflection-mode PA technique. The all-optical confocal geometry required by PARS is significantly easier to implement and maintain than the opto-acoustic geometry of conventional PA microscopy techniques. This results in a system capable of high resolution and sensitivity, imaging at real-time rates. The authors believe this work represents a vital step towards a clinical high-resolution reflection-mode video-rate PA imaging system.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".