Thermometry using entropy imaging of ultrasound radio frequency signal time series
Bibliographic record
Abstract
Low intensity focused ultrasound (LIFU) is a novel approach that could activate drug release and considerably improve the delivery of anticancer drug. LIFU treatment has some features like is able to penetrate deep into the tissue and being non-invasive, as a consequence LIFU displays great capability for controlling the drug release and improving the chemotherapy treatment efficiency. The goal of this study is to research the feasibility of the entropy parameter of RF time series of ultrasound backscattered signals for measuring the changes in temperature induced by a LIFU device. Entropy Imaging is a technique for reconstructing ultrasound images based on the average uncertainty of time-series in a signal. Furthermore, the Shannon Entropy can quantify the uncertainty of a random process and is usually used as a measure for the information content of probability distributions. In this study, we use the Entropy Imaging method for measuring the LIFU-induced temperature changes in the deep region of ex vivo porcine tissue samples. The results obtained show that the changes of entropy parameter of RF time series signal are proportional to temperature changes recorded by a calibrated thermocouple in the temperature range of 37–47°C. In conclusion, in this study we show that Shannon entropy of RF time series signal possesses promising features like succinctly capturing the available information in a system by considering the uncertainty in a given data that can be used, as a new method, to measure temperature changes non-invasively and quantitatively in the deep region of tissue.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".