Four-dimensional photoacoustic imaging of moving targets
Bibliographic record
Abstract
Photoacoustic imaging provides optical contrast with improved tissue penetration and spatial resolution compared to pure optical techniques. Three-dimensional photoacoustic imaging is particularly advantageous for visualizing non-planar light absorbing structures, such as blood vessels, internal organs or tumours. We have developed a fast 3-D photoacoustic imaging system for small animal research based on a sparse array of ultrasonic detectors and iterative image reconstruction. The system can acquire 3-D images with a single laser-shot at a frame rate of 10 Hz. To demonstrate the imaging capabilities we have constructed phantoms made of a scanning point source and a rotating line object and imaged them at a rate of 10 frames per second. The resulting 4-D photoacoustic images depicted well the motion of each target. Comparison of the perceived motion in the images with the known velocity of the target showed good agreement. To our knowledge, this is the first report of single-shot high frame-rate 3-D photoacoustic imaging system. With further developments, this system could bring to bear its inherent speed for applications in small animal research, such as motion tracking of tumour outline during respiration, and rapid monitoring of contrast agent kinetics.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".