Estimating dynamic changes of tissue attenuation coefficient during high-intensity focused ultrasound treatment
Bibliographic record
Abstract
BACKGROUND: This study investigated the dynamic changes of tissue attenuation coefficients before, during, and after high-intensity focused ultrasound (HIFU) treatment at different total acoustic powers (TAP) in ex vivo porcine muscle tissue. It further assessed the reliability of employing changes in tissue attenuation coefficient parameters as potential indicators of tissue thermal damage. METHODS: Two-dimensional pulse-echo radio frequency (RF) data were acquired before, during, and after HIFU exposure to estimate changes in least squares attenuation coefficient slope (Δβ) and attenuation coefficient intercept (Δα 0). Using the acquired RF data, Δβ and Δα 0 images, along with conventional B-mode ultrasound images, were constructed. The dynamic changes of Δβ and Δα 0, averaged in the region of interest, were correlated with B-mode images obtained during the HIFU treatment process. RESULTS: At a HIFU exposure duration of 40 s and various HIFU intensities (737-1,068 W/cm(2)), Δβ and Δα 0 increased rapidly to values in the ranges 1.5-2.5 dB/(MHz.cm) and 4-5 dB/cm, respectively. This rapid increase was accompanied with the appearance of bubble clouds in the B-mode images. Bubble activities appeared as strong hyperechoic regions in the B-mode images and caused fluctuations in the estimated Δβ and Δα 0 values. After the treatment, Δβ and Δα 0 values gradually decreased, accompanied by fade-out of hyperechoic spots in the B-mode images. At 10 min after the treatment, they reached values in ranges 0.75-1 dB/(MHz.cm) and 1-1.5 dB/cm, respectively, and remained stable within those ranges. At a long HIFU exposure duration of around 10 min and low HIFU intensity (117 W/cm(2)), Δβ and Δα 0 gradually increased to values of 2.2 dB/(MHz.cm) and 2.2 dB/cm, respectively. This increase was not accompanied with the appearance of bubble clouds in the B-mode images. After HIFU treatment, Δβ and Δα 0 gradually decreased to values of 1.8 dB/(MHz.cm) and 1.5 dB/cm, respectively, and remained stable at those values. CONCLUSIONS: Δβ and Δα 0 estimations were both potentially reliable indicators of tissue thermal damage. In addition, Δβ and Δα 0 images both had significantly higher contrast-to-speckle ratios compared to the conventional B-mode images and outperformed the B-mode images in detecting HIFU thermal lesions at all investigated TAPs and exposure durations.
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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 | 0.001 | 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".