Sci‐AM1 Sat ‐ 01: A system for MRI‐guided thermal therapy of prostate disease with transurethral ultrasound heating applicators
Bibliographic record
Abstract
An increasing number of prostate cancers are detected at an early stage when the tumour is localised within the gland. Image‐guided transurethral thermal therapy is a promising minimally‐invasive approach to treat targeted regions of the prostate gland while sparing surrounding tissue from unwanted damage. This work describes both a system developed to deliver MRI‐guided transurethral thermal therapy, and the characterisation of multi‐element ultrasound heating applicators. The delivery system includes five independent channels capable of producing up to 50W of RF power. Heating patterns produced by the device can be visualised during treatment using MRI thermometry and software developed to interface with the MR scanner. A fully MRI‐compatible motor has also been developed to rotate the heating applicator during treatment within the scanner. Evaluation of the capability to perform quantitative thermometry during heating with these heating applicators has been performed in a thermal gel material (TGM) developed in our lab with tissue‐mimicking ultrasound and thermal properties. This system has enabled us to validate MRI measurements of temperature distributions with the boundary of thermal coagulation in the TGM. In addition, verification of numerical simulations of heating has been accomplished and the feasibility of using this technology for conformal treatment of the prostate has been addressed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".