Viscoelastic transcranial wave propagation modeling for neuronavigated focused ultrasound procedures in humans
Bibliographic record
Abstract
Transcranial focused ultrasound (FUS) has gained significant attention as a non-invasive therapeutic technique that can reach both cortical and subcortical targets in the human brain. However, the skull barrier distorts the ultrasound beam and introduces significant losses that need to be accounted for. In this talk, I will present our efforts to develop high precise transcranial ultrasound modeling based on the open-source viscoelastic wave propagation solver BabelViscoFDTD and its application in neuronavigated procedures for FUS-based neuromodulation. The viscoelastic wave equation is expressed in isotropic stress tensors and displacement vectors, and their nodes are placed in a staggered grid arrangement. The solver uses a Cartesian O(4)-space O(2)-time FDTD scheme. A narrow-band quality factor models attenuation. A reduction of staircase artifacts can be enabled using a superposition operator. BabelViscoFDTD is optimized for multiple computing GPU and CPU backends (CUDA, OpenCL, Metal, and X86-64) and includes GPU-accelerated solvers of the Rayleigh-Sommerfeld equation and Bio-Heat thermal equations. We adapted the viscoelastic and thermal solvers to neuronavigate two FUS devices: A 128-element concaved array operable at 250 kHz and 700 kHz and a 4-ring concaved array operable at 500 kHz. The high degree of optimization of the solvers allow that numerical procedures (neuronavigation and modeling) can be performed in an Apple M1 Max (64 GB RAM) laptop computer.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".