Acoustic Field Simulation and Initial Safety Measurements of a Novel Ultrasound Probe for the Diagnosis of Intracranial Hemorrhages
Bibliographic record
Abstract
Ultrasound imaging of brain tissue features and abnormalities is notoriously difficult through the skull because of the high attenuation and skull-induced phase aberration. A custom-designed transcranial matrix probe with a novel adaptive beamforming method has been developed at Tessonics Inc., Windsor, Ontario. The technology corrects for skull-induced distortions in the reconstructed sonograms. The current goal is to diagnose intracranial hemorrhages with the motivation of reducing the time between an injury and appropriate aid. In emergency situations, a portable point-of-care device may save vital time and reduce fatalities. Conventional screening methods, like computed tomography, do not offer the portability, low-cost, and non-ionizing radiation benefits that ultrasound imaging provides. Ultrasonography is also non-invasive and allows for real-time imaging. The analysis of the acoustic field for the ultrasound probe is an integral part of device development. Standards for ultrasound machine intensity output must be met to eliminate the risk of damage to body tissues. Simulations of the acoustic field are created through the Fast Object-oriented C++ Ultrasound Simulation (FOCUS) software based on desired focal points. The measurements and simulations presented in this work will ensure patient safety for future testing.
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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 | 0.002 |
| 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 | 0.000 |
| 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".