High-resolution photoacoustic 3D imaging system for animal experiments using a hemispherical detector array
Bibliographic record
Abstract
A photoacoustic 3D imaging system for animal experiments was made. This system is special because it has a hemispherical detector array. To test its performance, we used a chart from the field of optics as a sample. We checked the whole imaging range using the ISO 12233 chart, which is used to test digital camera images. We found that there was no distortion in the xy-plane and the system had high resolution. We also tested it using a high image quality mode with a different scanning sequence. In this study, live albino mice with white hairs were anesthetized and photographed. Using hair removal cream, we were able to visualize the vascular network throughout their bodies, including blood vessels in organs such as the liver and kidneys. The smallest vessels we were able to visualize were less than 0.1 mm in diameter. We used photoacoustic (PA) images to relatively estimate the oxygen saturation of the mice's blood at two different wavelengths, which we refer to as the S-factor. By analyzing the PA images, we were able to estimate the arterial and venous systems of the whole body, as well as the difference in S-factor between the two systems within the liver. When the mice were euthanized and examined post-mortem, we observed that the S-factor of the whole body decreased and the difference in S-factor between the two systems within the liver was lost.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.006 | 0.002 |
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".