Feasibility of noninvasive thermometry in hyperthermia regime using harmonics generated by nonlinear ultrasound wave propagation
Bibliographic record
Abstract
Hyperthermia is a cancer treatment modality that could be delivered as a stand-alone treatment or in conjunction with chemotherapy or radiation therapy. Noninvasive and real-time temperature monitoring of the heated tissue improves the efficacy and safety of the treatment. Ultrasound-based thermometry requires a temperature-sensitive acoustic parameter that can be used to estimate the temperature by tracking changes in that parameter during heating. This dissertation describes the experiments and simulations performed to obtain the temperature dependence of acoustic harmonics generated by nonlinear ultrasound propagation in several media including: water, an attenuating tissue-mimicking liquid, ex vivo bovine muscle tissues, and tissue-mimicking gel phantoms. The mechanisms of action of harmonic generation in water and in the attenuating liquid, made by a mixture of 90% glycerol and 10% water (by volume), as a function of temperature at various frequencies have been investigated using a temperature dependent Khokhlov–Zabolotskaya–Kuznetsov (KZK) nonlinear acoustic wave propagation model. The simulation results were compared with and validated by measurements. In water, the harmonic amplitudes decrease with increasing the temperature at low frequencies (1 and 3.3 MHz), while the opposite temperature dependence was observed at higher frequencies (13 and 20 MHz). The harmonic generation significantly increased with temperature in the tissue-mimicking liquid at both frequencies of 5 and 13 MHz. The temperature dependence of harmonics in tissue-mimicking gel phantoms and ex vivo bovine muscle tissues were measured using a commercial high-frequency ultrasound imaging system, and a new noninvasive ultrasound-based thermometry has been developed that is based on the backscattered energy of the harmonics. The sensitivity of this new thermometry technique to medium’s motion was studied and compared with the conventional echo-shift thermometry technique. Based on this study, it is suggested that noninvasive temperature estimation is feasible using acoustic harmonics with lower sensitivity to motion artifacts compared to the conventional echo-shift technique.
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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 | 0.001 |
| 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".