Estimating optical absorption, scattering, and Grueneisen distributions with multiple-illumination photoacoustic tomography
Why this work is in the frame
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Bibliographic record
Abstract
While photoacoustic methods offer significant promise for high-resolution optical contrast imaging, quantification has thus far proved challenging. In this paper, a noniterative reconstruction technique for producing quantitative photoacoustic images of both absorption and scattering perturbations is introduced for the case when the optical properties of the turbid background are known and multiple optical illumination locations are used. Through theoretical developments and computational examples, it is demonstrated that multiple-illumination photoacoustic tomography (MI-PAT) can alleviate ill-posedness due to absorption-scattering nonuniqueness and produce quantitative high-resolution reconstructions of optical absorption, scattering, and Gruneisen parameter distributions. While numerical challenges still exist, we show that the linearized MI-PAT framework that we propose has orders of magnitude improved condition number compared with CW diffuse optical tomography.
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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 | 0.000 |
| 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