Detection of Hematoma Boundaries in Transcranial Ultrasound Brain Imaging via Envelope Reconstruction on Resonance-based Signal Decomposition
Bibliographic record
Abstract
TRUBI (Transcranial Ultrasound Brain Imaging) system is a 3D transcranial ultrasound brain imaging device from Tessonics Medical Systems to address the main limitations of conventional transcranial imaging, i.e., the highly distorting effects of human skull. Apart from the hardware design and interface software of the device in general, theoretical design, analysis and implementation of the signal processing unit of the system is one of the main challenges. In this paper, we briefly present part of the signal processing algorithm used in the prototype version of the TRUBI to extract desired features required to detect intracranial hemorrhages (ICH) during 3D transcranial ultrasound brain imaging. Difficulties in ultrasound-based transcranial imaging are mostly related to the fact that the skull attenuates and distorts acoustic signals dramatically. Despite the challenges, and unlike other available techniques, transcranial brain imaging with ultrasound is portable, radiation-free, not expensive and can be used easily not only in hospitals but also in clinics, emergency, ambulance and remote areas with no access to MRI or CT scanner.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".