DYNAMIC CHANGES IN THE ACOUSTO-MECHANICAL AND STATISTICAL PARAMETERS OF TISSUE DURING HIGH INTENSITY FOCUSED ULTRASOUND (HIFU) TREATMENT
Bibliographic record
Abstract
High intensity focused ultrasound (HIFU) induces focalized tissue coagulation by increasing the tissue temperature in a tight focal region and has been successfully used as a new technique of tumor treatment or to stop bleeding in clinical applications. The main challenges of this technique are: adjusting the location of HIFU thermal ablation exactly at the region of interest, and controlling the level of thermal ablation. Several imaging methods have been proposed to monitor HIFU-induced thermal lesions such as X-ray, MRI and ultrasound imaging. Currently, ultrasound imaging techniques that are clinically used for monitoring HIFU treatment are standard pulse-echo B-mode ultrasound imaging, ultrasound temperature estimation, and elastography-based methods. This study was carried on ex vivo animal tissue samples. Backscattered radio frequency (RF) signals were acquired in real-time including before, during and after HIFU treatment. In this study, first we estimate the dynamic changes in the acoustical, mechanical and statistical parameters of the tissue resulted from HIFU exposures with three different acoustic powers. Then, we use these parameters to detect the induced HIFU thermal lesions and monitor the treatment process. By estimating the standard deviation of the studied parameters along acquired RF data frames, we show that there are significant changes in the tissue properties during the HIFU treatment.
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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 | 0.000 | 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) | 0.000 | 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".