Visualization of ultrasound induced cavitation bubbles using the synchrotron x-ray Analyzer Based Imaging technique
Bibliographic record
Abstract
Observing cavitation bubbles deep within tissue is very difficult. The development of a method for probing cavitation, irrespective of its location in tissues, would improve the efficiency and application of ultrasound in the clinic. A synchrotron x-ray imaging technique, which is capable of detecting cavitation bubbles induced in water by a sonochemistry system, is reported here; this could possibly be extended to the study of therapeutic ultrasound in tissues. The two different x-ray imaging techniques of Analyzer Based Imaging (ABI) and phase contrast imaging (PCI) were examined in order to detect ultrasound induced cavitation bubbles. Cavitation was not observed by PCI, however it was detectable with ABI. Acoustic cavitation was imaged at six different acoustic power levels and six different locations through the acoustic beam in water at a fixed power level. The results indicate the potential utility of this technique for cavitation studies in tissues, but it is time consuming. This may be improved by optimizing the imaging method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".