<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 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 | 0.000 | 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) | 0.001 | 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".