Real time 3-D ultrasound diagnostic imaging system including 3-D adaptive beamforming processing
Bibliographic record
Abstract
This paper describes the analog front end module and the computing architecture components of a fully digital real-time 3-D ultrasound system. The computing architecture is designed to allow for an efficient implementation of a 3-D adaptive beamformer that has the capability to improve the angular image resolution of a planar array by approximately four times. The complex 3-D beamforming structure is decomposed into two steps of line array beamformers and this kind of decomposition process for the 3-D beamformer allows for its efficient implementation into the highly parallelized multi-processor based computing architecture for real time 3-D ultrasound imaging applications providing 20 volumes per second at a full opening angle of 80 deg (azimuth and elevation). The main objective of this paper is to describe the details of the system processing requirements and design consideration for the computing architecture and provide the experimental results showing that the proposed implementation can achieve the targeted frame rate. An easy to use user interface in combination with a decision-support process provides the possibility for a rapid and automated diagnosis of internal injuries like bleeding or facilitates image guided surgery.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".