A PVDF Receiver for Ultrasound Monitoring of Transcranial Focused Ultrasound Therapy
Bibliographic record
Abstract
Focused ultrasound (FUS) shows great promise for use in the area of transcranial therapy. Currently dependent on MRI for monitoring, transcranial FUS would benefit from a real-time technique to monitor acoustic emissions during therapy. A polyvinylidene fluoride receiver with an active area of 17.8 mm (2) and a film thickness of 110 mum was constructed. A compact preamplifier was designed to fit within the receiver to improve the receiver SNR and allow the long transmission line needed to remove the receiver electronics outside of the MRI room. The receiver was compared with a 0.5 mm commercial needle hydrophone and focused and unfocused piezoceramics. The receiver was found to have a higher sensitivity than the needle hydrophone, a more wideband response than the piezoceramic, and sufficient threshold for detection of microbubble emissions. Sonication of microbubbles directly and through a fragment of human skull demonstrated the ability of the receiver to detect harmonic bubble emissions, and showed potential for use in a larger scale array. Monitoring of disruption of the blood-brain barrier in rats showed functionality in vivo and the ability to detect subharmonic, harmonic, and wideband emissions during therapy. The receiver shows potential for monitoring acoustic emissions during treatments and providing additional parameters to assist treatment planning. Future work will focus on developing a multi-element array for transcranial treatment monitoring.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".