<scp>Echo‐Focusing</scp> in Transcranial Focused Ultrasound Thalamotomy for Essential Tremor: A Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Transcranial magnetic resonance-guided focused ultrasound (TcMRgFUS) systems currently employ computed tomography (CT)-based aberration corrections, which may provide suboptimal trans-skull focusing. OBJECTIVES: The objective of this study was to evaluate a contrast agent microbubble imaging-based transcranial focusing method, echo-focusing (EF), during TcMRgFUS for essential tremor. METHODS: A clinical trial of TcMRgFUS thalamotomy using EF for the treatment of essential tremor was conducted (NCT03935581; funded by InSightec [Tirat Carmel, Israel]). Patients (n = 12) were injected with Definity (Lantheus Medical Imaging, North Billerica, MA) microbubbles, and EF was performed using a research feature add-on to a commercial TcMRgFUS system (ExAblate Neuro, InSightec). Subablative thermal sonications carried out using (1) EF and (2) CT-based aberration corrections were compared via magnetic resonance thermometry, and the optimal focusing method for each patient was employed for TcMRgFUS thalamotomy. RESULTS: EF aberration corrections provided increased sonication efficiency, decreased focal size, and equivalent targeting accuracy relative to CT-based focusing. EF aberration corrections were employed successfully for lesion formation in all 12 patients, 3 of whom had previously undergone unsuccessful TcMRgFUS thalamotomy via CT-based focusing. There were no adverse events related directly to the EF procedure. CONCLUSIONS: EF is feasible and appears safe during TcMRgFUS thalamotomy for essential tremor and improves on the trans-skull focal quality provided by existing CT-based focusing methods. © 2020 International Parkinson and Movement Disorder Society.
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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".