Simulating transvertebral ultrasound propagation with a multi-layered ray acoustics model
Bibliographic record
Abstract
The simulation accuracy of transvertebral ultrasound propagation using a multi-layered ray acoustics model based on CT-derived vertebral geometry was investigated through comparison with experimental measurements of pressure fields in ex vivo human vertebral foramen. A spherically focused transducer (5 cm diameter, f-number 1.2, 514 kHz) was geometrically focused to the centre of individual thoracic vertebral foramen, through the posterior bony elements. Transducer propagation paths through the laminae and the spinous processes were tested. Simulation transducer-vertebra configurations were registered to experiment transducer-vertebra configurations, and simulation accuracy of the simulation model was evaluated for predicting maximum transmitted pressure to the canal, voxel pressure in the canal, and focal distortion. Accuracy in predicting maximum transmitted pressure was calculated by vertebra, and it is shown that simulation predicts maximum pressure with a greater degree of accuracy than a vertebra-specific insertion loss. Simulation error in voxel pressure was evaluated using root-mean-square error and cross-correlation, and found to be similar to the water-only case. Simulation accuracy in predicting focal distortion was evaluated by comparing experiment and simulation maximum pressure location and weighted >50% focal volume location. 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. These errors are small relative to the dimensions of the transducer focus (4.9 mm full width half maximum), the spinal cord (10 mm diameter), and vertebral canal diameter (15-20 mm diameter). These results suggest that ray acoustics can be applied to simulating transvertebral ultrasound propagation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".