An automated breast ultrasound scanner with integrated photoacoustic tomography
Bibliographic record
Abstract
We have integrated photo-acoustic imaging into an automated breast ultrasound scanner (ABUS) with the goal of simultaneously performing ultrasound (US) and multi-spectral photo-acoustic tomography (PAT). This was accomplished with minimal change to the existing automated scanner by coupling laser light into an optical fiber for flexible and robust light delivery. We present preliminary tomography data acquired with this setup, including a simple resolution-testing geometry and a tissue phantom. Integrating PAT into the ABUS such that breast imaging is possible will require illumination from below the transducer dome. To that end, we are moving towards a fiber-based, localized illumination geometry which is fixed relative to the transducer. By illuminating locally (only near the current acquisition slice), this approach reduces overall light exposure at the tissue surface, allowing higher light intensity per acquisition (which translates to higher absorber contrast), while remaining below safe exposure thresholds. We present time-domain simulations of photo-acoustic imaging under non-uniform illumination conditions, and test one potential weighting scheme which can be used to extract absorber locations.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".