Experimental and numerical comparison of multiple passive beamformers for separating intra- and extra-canal cavitation activity during transvertebral spinal cord therapy
Bibliographic record
Abstract
Distinguishing intra-canal cavitation activity during transvertebral focused ultrasound sonication of the spinal cord is a challenge due to the strong prefocal cavitation emissions in the spinalis musculature overwhelming emissions originating in the canal. To achieve mapping of all cavitation sources simultaneously, two methods for enhancing detection sensitivity are investigated: (1) multiple dynamic ranges within a reconstructed volume and (2) utilizing alternative beamformers to delay-and-sum (DAS) during map reconstruction. The performance of DAS beamforming is compared to a delay-multiply and-sum beamformer (DMAS), with and without a paired multiplicative compounding method (pDMAS). Experiments and simulations were performed on a 128-element, dual-aperture transvertebral array, through stacks of ex vivo human vertebra. Numerically and experimentally obtained point spread functions were compared in 3D, producing voxel-wise cross correlation values of 0.84, 0.89, 0.97 (N = 1) for the beamformers listed above, respectively, in water. Experimental, transvertebral localizations of canal sources in isolation produced localization error of 2.8 ± 1.2, 2.9 ± 1.4, 2.7 ± 1.3 mm for a single vertebral target (N = 30 sonications), respectively. A large numerical data set investigating the prefocal cavitation problem (N > 160) is presented and compared with (N = 9) experimental data demonstrating enhancement of intracanal sensitivity in cavitation maps.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".