Nakagami imaging for detecting thermal lesions induced by high-intensity focused ultrasound in tissue
Bibliographic record
Abstract
High-intensity focused ultrasound induces focalized tissue coagulation by increasing the tissue temperature in a tight focal region. Several methods have been proposed to monitor high-intensity focused ultrasound-induced thermal lesions. Currently, ultrasound imaging techniques that are clinically used for monitoring high-intensity focused ultrasound treatment are standard pulse-echo B-mode ultrasound imaging, ultrasound temperature estimation, and elastography-based methods. On the contrary, the efficacy of two-dimensional Nakagami parametric imaging based on the distribution of the ultrasound backscattered signals to quantify properties of soft tissue has recently been evaluated. In this study, ultrasound radio frequency echo signals from ex vivo tissue samples were acquired before and after high-intensity focused ultrasound exposures and then their Nakagami parameter and scaling parameter of Nakagami distribution were estimated. These parameters were used to detect high-intensity focused ultrasound-induced thermal lesions. Also, the effects of changing the acoustic power of the high-intensity focused ultrasound transducer on the Nakagami parameters were studied. The results obtained suggest that the Nakagami distribution's scaling and Nakagami parameters can effectively be used to detect high-intensity focused ultrasound-induced thermal lesions in tissue ex vivo. These parameters can also be used to understand the degree of change in tissue caused by high-intensity focused ultrasound exposures, which could be interpreted as a measure of degree of variability in scatterer concentration in various parts of the high-intensity focused ultrasound lesion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".