Design of HIFU Treatment Plans Using Thermodynamic Equilibrium Algorithm
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Bibliographic record
Abstract
High-intensity focused ultrasound (HIFU) is a non-invasive medical procedure, which is mainly used to ablate tumors externally by focusing on them with high-frequency ultrasound. Because a single ablation can process only a small volume of tissue, a succession of ablations is required to treat a large volume of cancerous tissue. In order to maximize the therapeutic effect and reduce side effects such as skin burns, careful preoperative treatment planning must be performed to determine the focal location and sonication time for each ablation. This paper proposes a novel optimization algorithm, called the thermodynamic equilibrium algorithm (TEA), inspired by the behavior of thermodynamic systems reaching their equilibrium states. Like other evolutionary algorithms, TEA starts with an initial population. Gas chambers at various thermodynamic states are employed as representatives of the population individuals, and the equilibrium state is regarded as the global minimum. The movement of thermodynamic parameters in the direction of reducing the temperature gradient forms the basis of the proposed evolutionary algorithm. During this movement, the second law of thermodynamics is checked to ensure that entropy will increase in each process. This movement leads to the state where most of the systems are at equilibrium. In this state, the systems are localized at the same position and have the same cost as the global minimum. The TEA was applied to several well-known unconstrained and constrained benchmark cost functions, and the performance was compared with other well-known optimization algorithms. The results showed that the TEA has high potential to handle various types of optimization problems with a good convergence rate and high precision. Finally, the suggested evolutionary approach is applied to HIFU treatment regimens adopting a map of patient-specific material properties and an accurate thermal model. High-quality treatment plans could be created using the suggested method, and the average amount of tissue that is over- or under-treated was less than 0.08 percent.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 it