Self-Sensing with Hollow Cylindrical Transducers for Histotripsy-Enhanced Aspiration Mechanical Thrombectomy Applications
Bibliographic record
Abstract
Intravascular aspiration thrombectomy catheters are widely used to treat stroke, pulmonary embolism, and deep venous thrombosis. However, their performance is frequently compromised by clot material becoming lodged within the catheter tip. To address this, we develop a novel ultrasound-enhanced aspiration catheter approach that generates cavitation within the tip to mechanically degrade clots, with a view to facilitate extraction. The design employs hollow cylindrical transducers that produce inwardly propagating cylindrical waves to generate sufficiently high pressures to perform histotripsy. This study investigates the feasibility of self-sensing cavitation detection by analyzing voltage signals across the transducer during treatment. Experiments were conducted for two transmit pulse lengths at varying driving voltages with water or clot in the lumen. Cavitation clouds within the lumen were assessed using 40 MHz ultrasound imaging. Changes in the signal envelope during the pulse body and ringdown phases occurred above the cavitation threshold, the latter being associated with more rapid wave damping in the presence of bubble clouds within the lumen. In the frequency domain, voltage-dependent cavitation signals-subharmonics, ultra-harmonics, and broadband-emerged alongside transmit pulses. This work demonstrates a highly sensitive, sensor-free method for detecting cavitation within the lumen, enabling feedback control to further improve histotripsy-assisted aspiration.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".