Analytical and Numerical Model of High Intensity Focused Ultrasound Enhanced With Nanoparticles
Bibliographic record
Abstract
OBJECTIVE: High intensity focused ultrasound (HIFU) is a new noninvasive therapeutics that allows local treatment of solid tumors through a hyperthermal mechanism using ultrasonic energy. One promising strategy to increase the thermal efficiency of HIFU is to employ nanoparticles (NPs) as ultrasound agents for the hyperthermia procedure. However, the interaction mechanism between NPs and ultrasonic waves has not been well understood. METHODS: In an effort to investigate the heating process of NPs-enhanced HIFU, we derived a set of HIFU equations governing the temperature variation during the thermal ablation based on the principle of conservation of energy for heat transfer mechanism. A numerical model was developed to solve the HIFU equations to simulate the absorption mechanism of HIFU in the presence of NPs, the consequent heat transfer process, and the temperature rise profile during the sonication period. The accuracy of numerical model was verified by performing a series of experiments on tissue-mimicking phantoms embedded with magnetic NPs (MNPs). RESULTS: The transport processes taking place at the boundaries between NPs and surrounding medium played the major role in the temperature rise during HIFU sonication. Besides, the effects of MNPs on rising temperature were improved by amplifying the ultrasonic power and frequency as well as by increasing the MNP concentration. CONCLUSION: A quantitative comparison with experimental results demonstrated the potential of the numerical model to accurately predict the heating mechanism of HIFU mediated by NPs. SIGNIFICANCE: The proposed method can help with simulation of HIFU when NPs are employed as ultrasound agents.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".