A Noninvasive Ultrasound Resonance Method for Detecting Skull Induced Phase Shifts May Provide a Signal for Adaptive Focusing
Bibliographic record
Abstract
OBJECTIVE: There may be a need to perform dynamic skull aberration corrections during the non-invasive high-intensity transcranial treatment with magnetic resonance imaging (MRI) -guided focused ultrasound in order to accurately and rapidly restore the focus in the brain. METHODS: This could possibly be accomplished by using an ultrasound-based correction method based on the skulls' thickness resonance frequencies. The focus of a 500 kHz transducer was centered in the ex vivo human skull caps at different temperatures. The pulse-echoed signals reflected from the skulls were analyzed in the frequency domain to reveal the resonance frequencies for the phase shift calculation. The accuracy was compared to both hydrophone and computed tomography (CT) based analytical methods. RESULTS: Around 73% of the measurements (n = 784) were in the optimal constructive interference region, with a 15° decrease in the average phase error compared to the previous study. In the best implementation, it performed approximately the same or better than the CT based analytical method currently in clinical use. Linear correlation was found between the resonance frequencies or skull induced phase shifts and the skull temperature with an average rate of -0.4 kHz/°C and 2.6 deg/°C, respectively. CONCLUSION: The ultrasound based resonance method has shown the feasibility of detecting heating-induced changes of skull phase shift non-invasively and accurately. SIGNIFICANCE: Since the technique can be made MRI compatible and integrated in the therapy arrays, it may enable temperature tracking and adaptive focusing during high-intensity transcranial ultrasound treatments, to prevent skull overheating and preserve the transcranial focusing integrity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".