3D photoacoustic imaging of a moving target
Bibliographic record
Abstract
We have developed a fast 3D photoacoustic imaging system based on a sparse array of ultrasound detectors and iterative image reconstruction. To investigate the high frame rate capabilities of our system in the context of rotational motion, flow, and spectroscopy, we performed high frame-rate imaging on a series of targets, including a rotating graphite rod, a bolus of methylene blue flowing through a tube, and hyper-spectral imaging of a tube filled with methylene blue under a no flow condition. Our frame-rate for image acquisition was 10 Hz, which was limited by the laser repetition rate. We were able to track the rotation of the rod and accurately estimate its rotational velocity, at a rate of 0.33 rotations-per-second. The flow of contrast in the tube, at a flow rate of 180 μL/min, was also well depicted, and quantitative analysis suggested a potential method for estimating flow velocity from such measurements. The spectrum obtained did not provide accurate results, but depicted the spectral absorption signature of methylene blue , which may be sufficient for identification purposes. These preliminary results suggest that our high frame-rate photoacoustic imaging system could be used for identifying contrast agents and monitoring kinetics as an agent propagates through specific, simple structures such as blood vessels.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".