Quantitative photoacoustic tomography with multiple optical sources
Why this work is in the frame
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Bibliographic record
Abstract
Quantitative imaging of optical properties of biological tissues with high resolution has been a long-sought-after goal of many research groups. Photoacoustic imaging is a hybrid bio-optical imaging technique offering optical absorption contrast with ultrasonic spatial resolution. While photoacoustic methods offer significant promise for high-resolution optical imaging, quantification has thus far proved challenging. In this paper, a noniterative reconstruction technique for producing quantitative photoacoustic images of absorption perturbations is introduced for the case when the optical properties of the turbid background are known and when multiple optical illumination locations are used. Through theoretical developments and computational examples it is demonstrated that multiple-optical-source photoacoustic imaging can produce quantitative optical absorption reconstructions. The combination of optical and photoacoustic measurements is shown to yield improved reconstruction stability.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it