A Spine-Specific Phased Array for Transvertebral Ultrasound Therapy: Design and Simulation
Bibliographic record
Abstract
OBJECTIVE: To design and simulate the performance of two spine-specific phased arrays in sonicating targets spanning the thoracic spine, with the objective of efficiently producing controlled foci in the spinal canal. METHODS: Two arrays (256 elements each, 500 kHz) were designed using multi-layered ray acoustics simulation: a four-component array with dedicated components for sonicating via the paravertebral and transvertebral paths, and a two-component array with spine-specific adaptive focusing. Mean array efficiency (canal focus pressure/water focus pressure) was evaluated using forward simulation in neutral and flexed spines to investigate methods that reduce spine-induced insertion loss. Target-specific four-component array reconfiguration and lower frequency sonication (250 kHz) were tested to determine their effects on array efficiency and focal dimensions. RESULTS: When neutral, two- and four-component efficiencies were [Formula: see text]% and [Formula: see text]%, respectively, spine flexion significantly increased four-component efficiency ([Formula: see text]%), but not two-component efficiency ([Formula: see text]%). Target-specific four-component re-configuration significantly improved efficiency ([Formula: see text]%). Both arrays produced controlled foci centered within the canal with similar 50% pressure contour dimensions: 10.8-11.9 mm (axial), 4.2-5.6 mm (lateral), and 5.9-6.2 mm (vertical). Simulation at 250 kHz also improved two- and four-component efficiency ([Formula: see text]% and [Formula: see text]%, respectively), but doubled the lateral focal dimensions. CONCLUSION: Simulation shows that the spine-specific arrays are capable of producing controlled foci in the thoracic spinal canal. SIGNIFICANCE: The complex geometry of the human spine presents geometrical and acoustical challenges for transspine ultrasound focusing, and the design of these spine-specific ultrasound arrays is crucial to the clinical translation of focused ultrasound for the treatment of spinal cord disease.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".