Computed tomography-based aberration correction for trans-skull acoustic focusing: Comparison of simulations and measurements with focused ultrasound brain systems
Bibliographic record
Abstract
Clinical focused ultrasound brain systems currently employ computed tomography (CT)-based phase and amplitude corrections to mitigate skull-induced distortions and restore an acoustic focus at the intended target. In this study, acoustic measurements were conducted using two transcranial magnetic resonance-guided focused ultrasound systems (ExAblate 4000, InSightec, Haifa, Israel) operating at 230 and 650 kHz. The acoustic fields generated by the devices within intact, water-filled ex-vivo human skulls near the geometric focus were mapped using a 0.5 mm diameter needle hydrophone. In addition, the signals transmitted from each individual array element were captured at various locations within the skull cavity. These measurements were repeated without the presence of the skull in order to determine the element-specific aberrations induced by the cranial bone for each target position. The experimental measurements were simulated using three previously developed transcranial ultrasound propagation models: an analytical method similar to that currently employed by clinical brain systems, a multi-layered ray-acoustic approach, and a three-dimensional full-wave propagation model based on the Westervelt equation. We will present a comparison of the models based on their computational complexity as well as their ability to both predict the phase and amplitude aberrations induced by the skull and reproduce the measured in-situ pressure field distributions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".