Three-dimensional photoacoustic imaging by sparse-array detection and iterative image reconstruction
Bibliographic record
Abstract
Photoacoustic imaging (PAI) has the potential to acquire 3-D optical images at high speed. Attempts at 3-D photoacoustic imaging have used a dense 2-D array of ultrasound detectors or have densely scanned a single detector on a 2-D surface. The former approach is costly and complicated to realize, while the latter is inherently slow. We present a different approach based on a sparse 2-D array of detector elements and an iterative reconstruction algorithm. This approach has the potential for fast image acquisition, since no mechanical scanning is required, and for simple and compact construction due to the smaller number of detector elements. We obtained spatial sensitivity maps of the sparse array and used them to optimize the image reconstruction algorithm. We then validated the method on phantoms containing 3-D distributions of optically absorbing point sources. Reconstruction of the point sources from the time-domain signals resulted in images with good contrast and accurate localization (< or =1 mm error). Image acquisition time was 1 s. The results suggest that 3-D PAI with a sparse array of detector elements is a viable approach. Furthermore, the rapid acquisition speed indicates the possibility of high frame rate 3-D PAI.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".