Bibliographic record
Abstract
Ultrafast ultrasound imaging overcomes the inherent limitations of conventional focused line-by-line acquisition by enabling exceptionally high frame rates, which are critical for dynamic tissue characterization and real-time applications.Initially, ultrafast imaging was implemented using unfocused plane waves (PWs) transmitted at multiple steering angles; however, this approach inherently limits the field of view (FOV) due to angular coverage constraints.Divergent wave imaging (DWI) has recently emerged as a promising alternative, wherein virtual sources are placed behind the transducer array to generate spherical wavefronts that insonify a broader region, effectively overcoming the FOV limitations of PW transmissions.In DWI, both the number and spatial distribution of virtual sources significantly influence image quality and frame rate.This study implements DWI using Field II simulations and systematically evaluates the impact of three virtual source distributions-linear, tilted, and curvilinear-under different transmission counts on image quality metrics, including lateral resolution and contrast ratio.The findings provide valuable insights into optimizing DWI parameters for improved image quality while balancing frame rate requirements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".