Predicting lesion size by accumulated thermal dose in MR‐guided focused ultrasound for essential tremor
Bibliographic record
Abstract
PURPOSE: To correlate the accumulated thermal dose (ATD) with lesion size in magnetic resonance (MR)-guided focused ultrasound (MRgFUS) thalamotomy to help guide future clinical treatments. MATERIALS AND METHODS: Thirty-six patients with medication-refractory essential tremor were treated using a commercial MRgFUS brain system (ExAblate 4000, InSightec) in a 3T MR scanner (MR750, GE Healthcare). Intraoperative MR-thermometry was performed to measure the induced temperature and thermal dose distributions (thermal coefficient = -0.00909 ppm/°C). The ATD was calculated over multiple sonications with appropriate corrections for spatial-shifting artifacts. The ATD profile sizes obtained for dose values of 17, 40, 100, 200, and 240 cumulative equivalent minutes at 43°C (CEM) were correlated with the corresponding lesion sizes measured via axial T1- and T2-weighted MR images acquired 1 day post-treatment. RESULTS: Of a total of 232 included sonications, 83 required corrections for off-resonance-induced spatial-shifting artifacts (correction range = [1.1,2.2] mm). The mean lesion sizes measured on T2-weighted MR images (6.2 ± 1.3 mm, mean ± SD) were 15% larger than those measured on corresponding T1-weighted MR images (5.3 ± 1.2 mm, mean ± SD). The ATD values that provided the best correlations with the measured lesion sizes on T2- and T1-weighted MR images were 100 and 200 CEM, respectively. CONCLUSION: The ATD was correlated with lesion size measured 1 day following MRgFUS thalamotomy for essential tremor. These data provide useful information for predicting brain lesion size and determining treatment endpoints in future clinical MRgFUS procedures.
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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 | 0.003 |
| 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 | 0.000 | 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 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".