Time-Course Characterization of High-Intensity Focused Ultrasound Exposure Using B-Mode Ultrasound Imaging
Bibliographic record
Abstract
High-Intensity Focused Ultrasound (HIFU) is an emerging non-invasive surgical technique. HIFU systems are generally designed to deliver a fixed amount of energy to tissue, but because the tissue thermal response is variable across patients and tissue type, this approach can often lead to underdosing or overdosing. In order to improve the dosing precision in HIFU we are investigating real time monitoring of the energy deposition process through high-frequency diagnostic ultrasound imaging. Ultimately the development of monitoring techniques will allow real-time adjustment of energy to obtain optimal treatment. We present results from an exploratory study into the transient response of HIFU exposure in phantoms and animal tissue. Imaging was performed with a Visual Sonics Vevo 2100 50 MHz ultrasound imaging system with 40µm resolution and a B-mode frame rate of 1kHz. HIFU deposition was performed in phantoms and chicken breast using a commercial HIFU system across a range of energy levels. HIFU deposition points had an exposure duration less than 50ms and a focal spot diameter less than 250µm. The high temporospatial resolution of the Vevo 2100 allows for a unique time-course characterization of the material response to HIFU exposure. The transient response observed in the B-mode ultrasound images is due to a combination of the thermal-acoustic lens effects, thermal expansion of the medium and cavitation. Images were acquired across a range of HIFU energy levels and analyzed to determine the energy dependence of various effects. These preliminary experiments will inform the development of new image analysis techniques for determining dosage received in tissue during HIFU exposure.
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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 | 0.001 |
| 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) | 0.001 | 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".