Receiver array design for sonothrombolysis treatment monitoring in deep vein thrombosis
Bibliographic record
Abstract
High intensity focused ultrasound (HIFU) can disintegrate blood clots through the generation and stimulation of bubble clouds within thrombi. This work examined the design of a device to image bubble clouds for monitoring cavitation-based HIFU treatments of deep vein thrombosis (DVT). Acoustic propagation simulations were carried out on multi-layered models of the human thigh using two patient data sets from the Visible Human Project. The design considerations included the number of receivers (32, 64, 128, 256, and 512), their spatial positioning, and the effective angular array aperture (100° and 180° about geometric focus). Imaging array performance was evaluated for source frequencies of 250, 750, and 1500 kHz. Receiver sizes were fixed relative to the wavelength (pistons, diameter = λ/2) and noise was added at levels that scaled with receiver area. With a 100° angular aperture the long axis size of the -3 dB main lobe was ~1.2λ-i.e. on the order of the vessel diameter at 250 kHz (~7 mm). Increasing the array aperture to span 180° about the geometric focus reduced the long axis by a factor of ~2. The smaller main lobe sizes achieved by imaging at higher frequencies came at the cost of increased levels of sensitivity to phase aberrations induced during acoustic propagation through the intervening soft tissue layers. With noise added to receiver signals, images could be reconstructed with peak sidelobe ratios < -3 dB using single-cycle integration times for source frequencies of 250 and 750 kHz (NRx ⩾ 128). At 1500 kHz, longer integration times and/or higher element counts were required to achieve similar peak sidelobe ratios. Our results suggest that a modest number of receivers(i.e. NRx = 128) arranged on a semi-cylindrical shell may be sufficient to enable passive acoustic imaging with single-cycle integration times (i.e. volumetric rates up to 0.75 MHz) for monitoring cavitation-based HIFU treatments of DVT.
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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 | 0.001 | 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".