Ex vivo validation of non-invasive phase correction for transspine focused ultrasound: model performance and target feasibility
Bibliographic record
Abstract
Abstract Objective. To evaluate the feasibility of transspine focused ultrasound using simulation-based phase corrections from a CT-derived ray acoustics model. Approach. Bilateral transspine focusing was performed in ex vivo human vertebrae with a spine-specific ultrasound array. Ray acoustics-derived phase correction was compared to geometric focusing and a hydrophone-corrected gold standard. Planar hydrophone scans were recorded in the spinal canal and three metrics were calculated: target pressure, coronal and sagittal focal shift, and coronal and sagittal Sørensen–Dice similarity to the free-field. Post hoc analysis was performed in silico to assess the impact of windows between vertebrae on focal shift. Main results. Hydrophone correction reduced mean sagittal plane shift from 1.74 ± 0.82 mm to 1.40 ± 0.82 mm and mean coronal plane shift from 1.07 ± 0.63 mm to 0.54 ± 0.49 mm. Ray acoustics correction reduced mean sagittal plane and coronal plane shift to 1.63 ± 0.83 mm and 0.83 ± 0.60 mm, respectively. Hydrophone correction increased mean sagittal similarity from 0.48 ± 0.22 to 0.68 ± 0.19 and mean coronal similarity from 0.48 ± 0.23 to 0.70 ± 0.19. Ray acoustics correction increased mean sagittal and coronal similarity to 0.53 ± 0.25 and 0.55 ± 0.26, respectively. Target pressure was relatively unchanged across beamforming methods. In silico analysis found that, for some targets, unoccluded paths may have increased focal shift. Significance. Gold standard phase correction significantly reduced coronal shift and significantly increased sagittal and coronal Sørensen–Dice similarity (p < 0.05). Ray acoustics-derived phase correction reduced sagittal and coronal shift and increased sagittal and coronal similarity but did not achieve statistical significance. Across beamforming methods, mean focal shift was comparable to MRI resolution, suggesting that transspine focusing is possible with minimal correction in favourable targets. Future work will explore the mitigation of acoustic windows with anti-focus control points.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".