Development of an omni-directional photoacoustic source for the characterization of a hemispherical sparse detector array
Bibliographic record
Abstract
Photoacoustic imaging systems that utilize small numbers of detectors and iterative reconstruction methods require sensitive calibration of the detector array. For each voxel-detector pair, this includes the time-of-flight, fullwidth-half-maximum, and signal amplitude. The objective of this work was to develop a photoacoustic point source which emitted signal uniformly in all directions such that these features can be precisely characterized to more accurately provide an estimate of the shape and position of an acoustic signal in the imaging volume. The source was placed equidistant from acoustic detectors at different zenith and azimuthal angles from a reference position where the acoustic signal could be captured and analyzed. In the zenith direction, the signal decreased in strength by approximately 32% over the range of angles (up to 67.5°). However, in the azimuthal direction, the signal varied substantially as the source was rotated in a stationary axial position indicating imperfections over the source surface that were created during the fiber polishing procedure. The source was used to characterize time-of-flight, full-width-half-maximum, and signal amplitude at a multitude of locations within the imaging volume. While characterization maps obtained with the point source provided reasonable results, the quality of the source could be improved by constructing a truly hemispherical tip on the fiber optic.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".