A ray acoustics-based simulation for predicting trans-vertebral ultrasound propagation: Simulation accuracy
Bibliographic record
Abstract
The blood-spinal cord barrier (BSCB) prevents drug delivery to spinal cord parenchyma. Focused ultrasound can temporarily increase BSCB permeability in small animal models. The human vertebral column distorts ultrasound foci, preventing clinical translation of this technique. Beamforming must be used to correct focal distortion and may be performed using ray acoustics and patient-specific preoperative CT scans. We evaluate the simulation accuracy of ray acoustics, applied to trans-vertebral ultrasound propagation, through comparison with experiment. Trans-vertebral ultrasound propagation was measured for a spherically focused transducer geometrically focused to the centre of individual thoracic vertebral foramen. Simulation pressures were compared to experiment pressures. Simulation error in voxel pressure was evaluated using root-mean-square error, and was similar to error in a water-only case. Average simulation error across all measurements and simulations in maximum pressure location and weighted >50% focal volume location were 2.3 mm and 1.5 mm, respectively. Simulation error is small relative to the dimensions of the transducer focus (4.9 mm full width half maximum), the spinal cord (8–10 mm diameter), and vertebral canal diameter (15–20 mm diameter), suggesting that ray acoustics may be sufficiently accurate for ultrasound beamforming to the vertebral foramen.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".