Comparison of passive beamformers for isolating cavitation activity originating in the spinal canal
Bibliographic record
Abstract
Focused ultrasound application to the spinal cord, through the intact spine, is confounded by the presence of substantial prefocal cavitation, in the posterior soft tissue and musculature. When constructing cavitation images to localize and control intracanal cavitation, the prefocal cavitation zones dwarf the signals from the canal, destructing the localization ability of the imaging inside the sensitive cord tissue—even when conventional phase and amplitude corrections are applied. In this study, multiple beamforming formulations are investigated to mitigate the influence of prefocal cavitation on transvertebral cavitation imaging, while preserving the localization ability in the canal. The performance of a standard delay-and-sum-integrate beamformer (DAS) is compared to delay-multiply-and-sum-integrate beamformer (DMAS), and the DMAS algorithm is tested with a paired multiplicative compounding method (pDMAS) that leverages the dual aperture approach required for transvertebral focused ultrasound sonications. The focal point spread function of the beamformers in passive cavitation images (tint = 7 μs) were 7.9 × 2.2 × 1.3 mm (DAS), 6.9 × 1.7 × 1.1 (DMAS), and 6.5 × 2.9 × 1.7 mm (pDMAS) at 800 kHz. These beamforming approaches are evaluated for localization ability and clinical feasibility through cavitation imaging of prefocal and focal sources with in silico models and ex vivo human vertebrae.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".