Development and characterization of an omni-directional photoacoustic point source for calibration of a staring 3D photoacoustic imaging system
Bibliographic record
Abstract
Photoacoustic imaging is a modality which makes use of the contrast provided by optical imaging techniques and the spatial resolution and penetration depth similar to acoustic imaging modalities. We have developed a method for fast 3D photoacoustic imaging using a sparse hemispherical array of transducers. Such a system requires characterization of the transducer's response to an ideal point source in order to accurately reconstruct objects in the imaging volume. First, an attempt was made to design an ideal photoacoustic point source via a combination of liquids which would appropriately scatter and absorb the light such that a spherical distribution was achieved. Methylene blue (MB(+)) was used as the primary optical absorber while Intralipid (IL) was used as the liquid responsible for the optical scatter. A multitude of combinations were tested and the signal uniformity was characterized. The combination of 200 microM MB(+) and 0.09% IL was found to produce the most uniform signal over the range of transducers in the hemispherical array. The liquid source was then characterized over a broader range of azimuthal and zenith angles where it was shown the azimuthal consistency was much greater than the stability seen in different zenith elevations. The source was then used in a calibration scan for an imaging volume of 40 x 40 x 40 mm(3). At 216 points evenly spaced in the imaging volume, parameters were recorded for signal amplitude, width, and time-of-flight. These calibration parameters could then be applied to an iterative reconstruction algorithm in an attempt to more accurately produce images.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".