Cost-effective radiation force balance for calibration of therapeutic ultrasound devices
Bibliographic record
Abstract
The objective was to create an inexpensive, portable, and accurate absorptive radiation force balance to measure acoustic powers of up to 100 watts generated by the high intensity focused ultrasound (HIFU) transducer. This paper describes the process of making an effective absorbing target with commercially available ingredients. Four different absorbing targets consisting of nickel powder, silicone elastomer, and microballoons were prepared and tested. Silicone Sylgard (Dow Corning, MI) was used for all samples. However, two different microballoons (acrylic and phenolic) and two different nickel powders (high density and spherical nickel powder) were used. The final results were compared with a commercially available reflection radiation force balance (RRFB). The results for the same 3.5 MHz HIFU transducer (Sonic Concepts, WA) revealed that a combination of spherical nickel powder (Alfa Aesar, MA) with acrylic microballoons (Douglas Sturgess, CA) offered an average efficiency of 89.8%, compared to that of RRFB, which was 80.7%. A combination of high density nickel powder (Inco Inc., Canada) with acrylic and phenolic microballoons were 82.5% and 84.2%, respectively (for the same HIFU transducer), while spherical nickel powder and phenolic microballoons (US Composites, FL) had efficiency of 64.4%, indicating incorrect measurements of HIFU transducer efficiency.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".