Measurement of photoacoustic transducer position by robotic source placement and nonlinear parameter estimation
Bibliographic record
Abstract
Source localization by photoacoustic tomography is dependent on time-of-flight pressure data collected by one or more transducers at multiple positions about the imaged object. Errors in transducer position lead directly to errors in source localization. The objective of this work was to develop a method for experimental determination of transducer position for the purpose of (i) comparison of the measured to the expected transducer position, and (ii) automated calibration of transducer position in scanning and array setups. Our approach was to acquire the time of arrival data at each transducer using a small, point-like photoacoustic source from many locations in the imaged volume. Source placement was controlled with a 3D robotic gantry (accuracy ±0.01 mm). Time of arrival data for all source locations was used to compute a vector of source-transducer distances. The coordinates of each transducer location were then found by nonlinear parameter estimation for a function that related the source distance to the known source location and the unknown transducer location. Application of the method to a planar array of 14 transducers resulted in identification of the position of each element in the transducer array. This finding suggested that the method may be useful for (i) mapping transducer positions during validation and calibration studies, (ii) measuring the effective position of transducers that are asymmetric or have fabrication errors, and (iii) obtaining the mapping relationship between the imaging system and the imaging volume in situations where coregistration of image data from other modalities is desired.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".