Development of robust/predictive control strategies for image-guided ablative treatments using a minimally invasive ultrasound applicator
Bibliographic record
Abstract
PURPOSE: One important challenge in image-guided ablative therapies is the effect of heat diffusion which can cause damage to surrounding organs and limit the ability to achieve a conformal pattern of thermal damage. Furthermore, tissue properties such as perfusion and energy absorption can be dynamic and difficult to measure. This paper attempts to address these problems by proposing new control methods. MATERIALS AND METHODS: A novel predictive approach was developed to compensate for the effect of heat diffusion using a minimally invasive rotating ultrasound heating applicator for ablative therapy. This method can be merged into any closed-loop control strategy. A binary controller, a previously developed adaptive proportional-integral controller, and a model reference adaptive controller were employed and compared, all with the predictive element incorporated. The reason for choosing these controllers was that none of them needed a model of the tissue or exact values of their parameters. RESULTS: The effectiveness of these controllers was demonstrated through both simulation and experimental studies. The results were consistent and demonstrated equivalent performance between controllers. CONCLUSIONS: The dominant influence on radial targeting accuracy was the prediction element described in this paper. A binary controller with a predictive element may provide the best balance of performance and simplicity for this application.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".