An Ultrasonic Adaptive Beamforming Method and Its Application for Trans-Skull Imaging of Certain Types of Head Injuries; Part II: Reception Mode and Adaptive Imaging
Bibliographic record
Abstract
OBJECTIVE: Background theory and a new algorithm for single-point adaptive focusing in transmission mode through ultrasonic barriers via one-dimensional phased arrays were reported in part I. In this paper the algorithm is further extended and implemented into a full adaptive beamforming process, including complete transmission and reception modes. METHODS: Corrected time delay patterns, adapted to the local acoustical and geometrical properties of the barrier, are calculated and applied in both modes. Further, an adaptive imaging process is also developed that implements the proposed beamforming process for two-dimensional imaging through randomly shaped multilayered phase-aberrating structures. The method is optimized for the case of human skull as the ultrasound barrier and its application for transcranial imaging is discussed. RESULTS: Laboratory results of adaptive imaging through realistic skull-mimicking phantoms are presented. The algorithms are implemented on a 64-channel ultrasound open-source phased array platform controlling a standard 128-element biomedical phased array. Irregularly shaped reflectors with characteristic dimensions of the order of ∼0.5 mm to ∼4.5 mm were used as targets behind the skull phantoms in our experiments. Minimum and maximum distortional target displacements of 2.2 mm and 25.3 mm (in 12 cm depth) were observed in sonograms when uncompensated time delays were used. By contrast, the positioning errors ranged from 0.0 to 0.9 mm when our algorithm was employed. CONCLUSION AND SIGNIFICANCE: The adaptive imaging results demonstrate strong potential of the proposed technique for diagnostic imaging of acoustically reflective head injuries directly through intact human skull.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".