TOBE-Costas Arrays for Fast High-Resolution 3-D Power Doppler Imaging
Bibliographic record
Abstract
Two-dimensional sparse arrays and row-column arrays are both alternatives to 2-D fully addressed arrays with lower channel counts. Row-column arrays have recently demonstrated fast 3-D structural and flow imaging but commonly suffer from high grating lobes or require multiplexing to achieve better quality. Two-dimensional sparse arrays enable full-volume acquisitions for each transmit event, but plane-wave transmissions with them usually lack quality in terms of uniformity of wavefronts. Here, we propose a novel architecture that combines both types of these arrays in one aperture, enabling imaging using row-column or sparse arrays alone or a hybrid imaging scheme where the row-column array is used in transmission and a 2-D sparse array in reception. This hybrid imaging scheme can potentially solve the shortcomings of each of these approaches. The sparse array layout chosen is a Costas array, characterized by having only one element per row and column, facilitating its integration with row-column arrays. We simulate images acquired with TOBE-Costas arrays using the hybrid imaging scheme and compare them to row-column and sparse spiral arrays of equivalent aperture size (128λ × 128λ at 7.5 MHz) in ultrafast plane-wave imaging of point targets and 3-D power Doppler imaging of synthetic flow phantoms. Our simulation results show that TOBE-Costas arrays exhibit superior resolution and lower sidelobe levels compared with plane-wave compounding with row-column arrays. Compared with density-tapered spiral arrays, they provide a larger field of view and finer resolution.
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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 | 0.000 | 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) | 0.002 | 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".