Bibliographic record
Abstract
Previously we described the potential for multiple illumination photoacoustic tomography to provide quantitative reconstructions, however this work used only simulated data. We have developed a custom photoacoustic-ultrasound tomography system capable of multiple illuminations and parallel acquisition from a 256 element 5 MHz transducer ring array with 8-cm diameter. The multiple illumination scheme uses a free-space light delivery geometry where a rotational stage scans a pulsed laser beam onto different incident locations around the sample. For each illumination location a photoacoustic image is reconstructed using a modified backprojection algorithm. Images from different source locations have the potential to be combined to form an improved deep-tissue image using our previously developed iterative algorithms. We complement the photoacoustic imaging data with unique ultrasound imaging data. Most previous ultrasound tomography methods have used migration algorithms, iterative ray-based analysis, wave-equation modeling, or frequency-based algorithms that all demand large amounts of data and computational power. We propose a new UST method that offers isotropic resolution, provides scattering contrast, as well as the potential for measuring ultrasound scattering anisotropy and decoupling density and compressibility contributions. The imaging system is driven by a Verasonics scan engine and programmed for both ultrasound and photoacoustic imaging modes. Resolution has been measured to be 150 μm for ultrasound and 200 μm for photoacoustic images. Imaging capabilities are demonstrated on phantoms with custom-tailored ultrasound scattering and optical properties, as well as in murine models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".