A Metallic Additively Manufactured Metamaterial for Enhanced Monitoring of Acoustic Cavitation‐Based Therapeutic Ultrasound
Bibliographic record
Abstract
The combination of ultrasound and microbubbles allows treatment of indications that would be impossible or too risk adverse with conventional surgery. During treatment, subharmonic and ultraharmonic components that can only be generated from microbubbles are of great interest for intraoperative monitoring. However, the microbubble emissions are several orders of magnitude lower in power compared to that of the fundamental frequency component from the ultrasound applicator, resulting in a low signal‐to‐noise ratio (SNR) for monitoring. A 3D acoustic metamaterial (AMM) immersed in water is proposed for suppressing unwanted ultrasound waves, which allows the improved sensitivity for detecting weak microbubble emissions. Numerically, the importance of shear waves on the AMM transfer properties is highlighted, though only longitudinal ultrasound waves are transmitted through water. Experimentally, the design is implemented in titanium using additive manufacturing, with an attenuation level of 40 dB at the fundamental frequency. Consequently, the application of the AMM efficiently improves the SNR for subharmonic and ultraharmonic microbubble emissions by 11.8 and 11.9 dB, respectively. The subharmonic components originally overwhelmed by noise are recovered. This is the first time that AMMs have been applied to passive acoustic monitoring and this work stands to improve treatment outcomes from cavitation‐mediated focused ultrasound therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".