Development of a new control strategy for 3D MRI‐controlled interstitial ultrasound cancer therapy
Bibliographic record
Abstract
PURPOSE: MRI-controlled interstitial ultrasound therapy is being developed as a minimally invasive, image-guided treatment for localized cancers. The method uses an interstitial multielement ultrasound applicator to deliver high-intensity ultrasound energy to tissue in order to achieve thermal coagulation in a target volume. METHODS: A new temperature feedback control algorithm incorporating a proportional-integral controller is introduced to tackle a multiple-input single-output control problem arising in MRI-controlled interstitial ultrasound cancer therapy. The inputs to the controller block are the frequency, rotation rate, and applied power of an interstitial applicator and the output is the boundary temperature during treatment. Multiplanar magnetic resonance (MR) thermometry is acquired continuously during heating and used in the feedback control algorithm to achieve spatial control over treatment. RESULTS: The method has been evaluated for prostate cancer treatment as an initial clinical application. Spatial treatment accuracy of a few millimeters is demonstrated in both simulations and experiments with the new controller. The spatial treatment accuracy of the new algorithm is shown to be equivalent or slightly improved over the existing approach implemented for this technology; however, the implementation of the new algorithm is much simpler, and does not involve time-intensive tuning of gain constants. CONCLUSIONS: The study demonstrates the potential advantages of a new automatic temperature control system adapted to image guided interstitial ultrasound therapy.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".