A Custom-Built Piezo-Optical System for Visualization and Characterization of High Intensity Focused Ultrasound
Bibliographic record
Abstract
Abstract High intensity focused ultrasound (HIFU) is a noninvasive treatment technique to induce thermal or mechanical bioeffects. Characterising the wave field is essential for reliable and reproducible transducer operation in clinical use. This paper presents a schlieren technique to visualise and quantify transducer wave fields. The technique is based on the piezo-optical effect of water, i.e. the refractive index variation caused by sound pressure. Our custom-built system equipped with a Raspberry Pi HQ camera can capture schlieren photographs of acoustic fields generated by a clinical HIFU system. Alternatively, a high-speed camera allows analysis of short burst pulses. We investigated the focal zone shape of continuously generated HIFU fields at acoustic powers of 10- 250 W. Images of the focal area at 100 W indicated dimensions comparable to the reported literature values. Notably, as power increases, we observe waveform distortion in the focal zone due to nonlinear propagation of ultrasound. Our findings demonstrate the efficacy of our system in visualizing and characterizing the acoustic fields generated by a clinical HIFU device.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".