Feasibility of detecting change in backscattered energy of acoustic harmonics in locally heated tissues
Bibliographic record
Abstract
Purpose: A real-time noninvasive thermometry technique is required to estimate the temperature distribution during hyperthermia to monitor and control the treatment. The main objective of this study is to demonstrate the possibility of detecting change in backscatter energy (CBE) of acoustic harmonics in tissue-mimicking gel phantoms and ex vivo bovine muscle tissues in which the temperature was locally increased within the hyperthermia regime.Materials and Methods: A peristaltic pump was used to circulate hot water through a needle inserted inside the samples to locally increase the temperature from 26 °C to 46 °C. The CBE of acoustic harmonics were used to identify the location of temperature changes in the samples. A conventional echo-shift technique was also implemented for comparison. Data collection was performed for two conditions to investigate the effect of motion on both techniques by: (1) inducing vibration in the sample through the peristatic pump and, (2) subsequently with no sample vibration while the pump was off.Results: Harmonics were able to determine the location of temperature rise in the presence and absence of vibration. In gel phantom, the mean contrast to noise ratio (CNR) in CBE maps reduced by a factor of 0.86 due to vibration whereas in gradient maps the CNR reduced by a factor of 8.3.Conclusions: The findings of this study suggest that the change in backscatter energy of acoustic harmonics can potentially be used to develop a noninvasive ultrasound-based thermometry technique with lower susceptibility to motion artifacts compared to the echo-shift method.
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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) | 0.001 | 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".