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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".