Suitability of a tumour-mimicking material for the evaluation of high-intensity focused ultrasound ablation under magnetic resonance guidance
Bibliographic record
Abstract
This study tests the suitability of a tumour-mimic for targeting magnetic resonance (MR)-guided high-intensity focused ultrasound (HIFU). An agarose-based tumour-mimic was injected as a warm solution that polymerized in tissue. Thermal characteristics and acoustic absorption of the mimic were observed within the values reported for tissues. The relaxation times at 3T were 1679 ± 15 ms for T1 and 41 ± 1 ms for T2. The mimic was clearly visible on in vivo images. With lower contrast the tumour-mimic was visible on T2-weighted images, where it was possible to detect the ablated tissue surrounding the mimic after sonications. HIFU sonications were performed to induce thermal ablation on and around the mimic using a Sonalleve system (Philips). MR thermometry maps were performed during HIFU. The average temperature when the sonication was done at the tumour-mimic was 67.6 ± 8.0 °C in vitro and 67.6 ± 5.0 °C in vivo. The average temperature for sonications at tissues was 68.4 ± 8.7 °C in vitro (liver) and 66.0 ± 2.6 °C in vivo (muscle), with no significant difference between tissue and tumour-mimic (p > 0.05). The tumour-mimic behaviour when using MR-guided HIFU was similar to tissues, showing that this mimic can be used as an alternative to tumour models for validating MR-guided HIFU devices targeting.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".