Ultrasound-guided microbubble-mediated brain therapy with a modular transmit/receive phased array
Bibliographic record
Abstract
The development of low-cost, ultrasound-guided focused ultrasound (USgFUS) treatment platforms is expected to advance the adoption of microbubble (MB)-mediated brain therapy by improving access to the technology. Our group has designed and fabricated clinical-scale transmit/receive phased array systems for MB-mediated USgFUS brain therapy. Acoustic field simulations were carried out to optimize array element placement, and transducer scaffolds were constructed using 3D printing techniques. These devices have been employed for skull computed tomography-array registration, 3D spatial mapping of MB activity in vivo through ex-vivo human skullcaps via noninvasive aberration correction methods, and we have harnessed this spatiotemporal cavitation information to calibrate exposure levels for safe volumetric blood-brain barrier opening. At higher exposure levels, we have demonstrated the ability of 3D MB imaging data to predict the tissue damage volume distributions induced during nonthermal brain ablation. Ultrafast processing of acoustic emissions data has been shown to uncover MB dynamics hidden by conventional whole-burst temporal averaging, and can inform temporal undersampling strategies when millisecond-long tone bursts are applied. Machine learning approaches can assist with image-based classification of MB activity, which may result in finer control of the induced bioeffects. This talk will focus on our recent results obtained with a novel modular USgFUS phased array system.
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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 | 0.000 | 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".