Registration of human skull computed tomography data to an ultrasound treatment space using a sparse high frequency ultrasound hemispherical array
Bibliographic record
Abstract
PURPOSE: Transcranial focused ultrasound (FUS) shows great promise for a range of therapeutic applications in the brain. Current clinical investigations rely on the use of magnetic resonance imaging (MRI) to monitor treatments and for the registration of preoperative computed tomography (CT)-data to the MR images at the time of treatment to correct the sound aberrations caused by the skull. For some applications, MRI is not an appropriate choice for therapy monitoring and its cost may limit the accessibility of these treatments. An alternative approach, using high frequency ultrasound measurements to localize the skull surface and register CT data to the ultrasound treatment space, for the purposes of skull-related phase aberration correction and treatment targeting, has been developed. METHODS: A prototype high frequency, hemispherical sparse array was fabricated. Pulse-echo measurements of the surface of five ex vivo human skulls were made, and the CT datasets of each skull were obtained. The acoustic data were used to rigidly register the CT-derived skull surface to the treatment space. The ultrasound-based registrations of the CT datasets were compared to the gold-standard landmark-based registrations. RESULTS: The results show on an average sub-millimeter (0.9 ± 0.2 mm) displacement and subdegree (0.8° ± 0.4°) rotation registration errors. Numerical simulations predict that registration errors on this scale will result in a mean targeting error of 1.0 ± 0.2 mm and reduction in focal pressure of 1.0% ± 0.6% when targeting a midbrain structure (e.g., hippocampus) using a commercially available low-frequency brain prototype device (InSightec, 230 kHz brain system). CONCLUSIONS: If combined with ultrasound-based treatment monitoring techniques, this registration method could allow for the development of a low-cost transcranial FUS treatment platform to make this technology more widely available.
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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".