Simulations of transcranial passive acoustic mapping with hemispherical sparse arrays using computed tomography-based aberration corrections
Bibliographic record
Abstract
Passive acoustic mapping (PAM) is receiving increasing interest as a method for monitoring focused ultrasound (FUS) therapy. PAM would be beneficial during transcranial cavitation-enhanced FUS treatments, particularly non-thermal, cavitation-mediated applications such as FUS-induced blood-brain barrier disruption or sonothrombolysis, for which no real-time monitoring technique currently exists. However, the use of PAM in the brain is complicated by the presence of the skull bone. If not properly accounted for, skull-induced aberrations of propagating cavitation emissions will lead to image distortion and artifacts upon reconstruction. Through the use of numerical simulations, this study investigated the feasibility of transcranial PAM via hemispherical sparse hydrophone arrays. A multi-layered ray acoustic transcranial ultrasound propagation model based on computed tomography (CT)-derived skull morphology was developed. By incorporating skull-specific aberration corrections into a conventional passive beamforming algorithm [Norton and Won, IEEE Trans. Geosci. Remote Sens. 38, 1337-1343 (2000)], simulated acoustic source fields were spatially mapped through digitized human skulls. The effects of array sparsity and receiver element configuration on the formation of passive acoustic maps were examined. Multiple source locations were simulated to determine the imageable volume within the skull cavity.
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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 | 0.001 |
| 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".