Transcranial Magnetic Resonance Imaging– Guided Focused Ultrasound Surgery of Brain Tumors
Bibliographic record
Abstract
OBJECTIVE: This work evaluated the clinical feasibility of transcranial magnetic resonance imaging-guided focused ultrasound surgery. METHODS: Transcranial magnetic resonance imaging-guided focused ultrasound surgery offers a potential noninvasive alternative to surgical resection. The method combines a hemispherical phased-array transducer and patient-specific treatment planning based on acoustic models with feedback control based on magnetic resonance temperature imaging to overcome the effects of the cranium and allow for controlled and precise thermal ablation in the brain. In initial trials in 3 glioblastoma patients, multiple focused ultrasound exposures were applied up to the maximum acoustic power available. Offline analysis of the magnetic resonance temperature images evaluated the temperature changes at the focus and brain surface. RESULTS: We found that it was possible to focus an ultrasound beam transcranially into the brain and to visualize the heating with magnetic resonance temperature imaging. Although we were limited by the device power available at the time and thus seemed to not achieve thermal coagulation, extrapolation of the temperature measurements at the focus and on the brain surface suggests that thermal ablation will be possible with this device without overheating the brain surface, with some possible limitation on the treatment envelope. CONCLUSION: Although significant hurdles remain, these findings are a major step forward in producing a completely noninvasive alternative to surgical resection for brain disorders.
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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) | 0.001 | 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) | 0.002 | 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".