Ultrasonic Imaging of Foreign Inclusions and Blood Vessels Through Thick Skull Bones
Bibliographic record
Abstract
We report a new progress in the development of a portable ultrasonic transcranial imaging system, which is expected to significantly improve the clinical utility of transcranial diagnostic ultrasound. When conventional ultrasonic phased array and Doppler techniques are applied through thick skull bones, the ultrasound field is attenuated, deflected, and defocused, leading to image distortion. To address these deficiencies, the ultrasonic transcranial imaging system implements two alternative ultrasonic methods. The first method improves detection of small foreign objects, such as bone fragments, pieces of shrapnel, or bullets, lodged in the brain tissue. Using adaptive beamforming, the method compensates for phase aberration induced by the skull and refocuses the distorted ultrasonic field at the desired location. The second method visualizes the blood flow through intact human skull using ultrasonic speckle reflections from the blood cells, platelets, or contrast agents. By analyzing these random temporal changes, it is possible to obtain 2D or 3D blood flow images, despite the adverse influence of the skull. Both methods were implemented on an advanced open platform phased array controller driving linear and matrix array probes. They were tested on realistic skull bone and head phantoms with foreign inclusions and blood vessel models.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".