Blanket illumination vs scanned-mosaicking imaging schemes for wide-area photoacoustic tomography
Bibliographic record
Abstract
We compare scanned-mosaicking and blanket illumination schemes for wide-field photoacoustic tomography with potential applications to breast imaging. For each illumination, a locally high-SNR image patch is reconstructed then mosaicked with image patches from other illuminations. Because the beam is not diffused over the entire area, the fluence of the beam can be maximized, therefore maximizing the signal generated. Moreover, the imaging can potentially still be done fast enough within a breath-hold. A Monte Carlo simulation as a function of beam-spot size and depth is performed to quantify this signal gain. We experimentally test both schemes using a 256-element Imasonic ring array on a tissue-mimicking phantom. We were able to verify the simulated signal gain of 2.9x under 0.5 cm of tissue with the experimental data, and measured the signal gain decrease expected when imaging deeper into the tissue. We also measured the effectiveness of averaging the diffused beam versus the scanned-mosaicking approach, and observed that for the same scan times and limited laser power output, scanned-mosaicking was able to produce a higher SNR than the blanket illumination approach. We have shown that this technique will allow wide-area PAT to utilize the maximum SNR available from any system while minimizing the number of acquisitions to reach this SNR.
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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 | 0.002 |
| 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 | 0.000 | 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".