Dynamic contrast-enhanced 3D photoacoustic imaging
Bibliographic record
Abstract
Photoacoustic imaging (PAI) is a hybrid imaging modality that integrates the strengths from both optical imaging and acoustic imaging while simultaneously overcoming many of their respective weaknesses. In previous work, we reported on a real-time 3D PAI system comprised of a 32-element hemispherical array of transducers. Using the system, we demonstrated the ability to capture photoacoustic data, reconstruct a 3D photoacoustic image, and display select slices of the 3D image every 1.4 s, where each 3D image resulted from a single laser pulse. The present study aimed to exploit the rapid imaging speed of an upgraded 3D PAI system by evaluating its ability to perform dynamic contrast-enhanced imaging. The contrast dynamics can provide rich datasets that contain insight into perfusion, pharmacokinetics and physiology. We captured a series of 3D PA images of a flow phantom before and during injection of piglet and rabbit blood. Principal component analysis was utilized to classify the data according to its spatiotemporal information. The results suggested that this technique can be used to separate a sequence of 3D PA images into a series of images representative of main features according to spatiotemporal flow dynamics.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".