Application of analyzer based X-ray imaging technique for detection of ultrasound induced cavitation bubbles from a physical therapy unit
Bibliographic record
Abstract
BACKGROUND: The observation of ultrasound generated cavitation bubbles deep in tissue is very difficult. The development of an imaging method capable of investigating cavitation bubbles in tissue would improve the efficiency and application of ultrasound in the clinic. Among the previous imaging modalities capable of detecting cavitation bubbles in vivo, the acoustic detection technique has the positive aspect of in vivo application. However the size of the initial cavitation bubble and the amplitude of the ultrasound that produced the cavitation bubbles, affect the timing and amplitude of the cavitation bubbles' emissions. METHODS: The spatial distribution of cavitation bubbles, driven by 0.8835 MHz therapeutic ultrasound system at output power of 14 Watt, was studied in water using a synchrotron X-ray imaging technique, Analyzer Based Imaging (ABI). The cavitation bubble distribution was investigated by repeated application of the ultrasound and imaging the water tank. The spatial frequency of the cavitation bubble pattern was evaluated by Fourier analysis. Acoustic cavitation was imaged at four different locations through the acoustic beam in water at a fixed power level. The pattern of cavitation bubbles in water was detected by synchrotron X-ray ABI. RESULTS: The spatial distribution of cavitation bubbles driven by the therapeutic ultrasound system was observed using ABI X-ray imaging technique. It was observed that the cavitation bubbles appeared in a periodic pattern. The calculated distance between intervals revealed that the distance of frequent cavitation lines (intervals) is one-half of the acoustic wave length consistent with standing waves. CONCLUSION: This set of experiments demonstrates the utility of synchrotron ABI for visualizing cavitation bubbles formed in water by clinical ultrasound systems working at high frequency and output powers as low as a therapeutic system.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".