Treatment monitoring for sonothrombolysis in deep vein thrombosis: Receiver array design
Bibliographic record
Abstract
The current treatment standard for deep vein thrombosis (DVT) is anticoagulation therapy, which does little to address long term morbidity compared to clot removal approaches. High intensity ultrasound can resolve clots by generating bubble clouds that erode thrombi. However, lack of appropriate treatment monitoring is a limiting factor in its widespread adoption. Passive cavitation imaging is capable of high frame rate, volumetric imaging, the combination of which has been shown to be important for monitoring the onset and development of bubble clouds inducing clot erosion (Acconcia et al. 2017). The results from our previous work motivated development of a device tailored to DVT with a relevant geometry (e.g., semi-cylindrical). Transmit simulations for such a device were conducted in a human thigh model (Smirnov and Hynynen 2017) with a design based on the modular transducer technology developed in our lab (Ellens et al. 2015). Here, we examine the integration of a sparse, randomly distributed receiver array within a semi-cylindrical arrangement of transmit modules for acoustic-based monitoring. Using a multi-layered propagation model, cavitation sources were localized to the femoral vessel, the accuracy of which depended on the inclusion of phase corrections. The receiver size was shown to be an important consideration with implicit trade-offs between directivity and channel SNR. Volumetric rates of ~1 MHz should be achievable with a modest number of receivers (128) in the presence of experimentally derived noise conditions.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".