Characterization of changes in therapeutic ultrasound transducer performance over time using the angular spectrum method
Bibliographic record
Abstract
Strongly focused large aperture transducers used in high-intensity focused ultrasound treatments are prone to manufacturing defects and degradation. Current methods for evaluating transducer quality measure only bulk physical changes of transducers. We have determined the pressure distribution at the transducer surface, using the angular spectrum method, to detect defects of the transducer. Three therapeutic transducers were investigated. The pressure distribution at the focal plane of each transducer was measured and input into a back-projection algorithm to calculate the pressure distribution at the transducer surface. A number of scan window sizes were used for the pressure distribution measurement at the focal plane to determine the effect on the resolution of the calculated pressure distribution at the transducer surface. Results showed that one transducer might have suffered manufacturing defects. The second transducer degraded over 1 year of use with one half of the transducer suffering a partial loss of efficiency. The third transducer remained unchanged over 1 year. The scan window of 40 mm X 40 mm at the focal plane was required to identify defects 6 mm in diameter on the transducer surface. The results demonstrate that the angular spectrum method could be a useful tool for evaluating transducer quality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".