Fast and mechanistic ultrasound simulation using a point source/receiver approach
Bibliographic record
Abstract
Ultrasound simulators relying on impulse response methods are faithful to the mechanisms of image formation from the underlying radio-frequency signals, but as a result tend to be relatively slow. At the other extreme are fast techniques, often motivated by the development of teaching and training simulators, which approximate the image formation processes rather than rigorously modeling the underlying physics. Previously, we have shown that transmit field distributions from linear phased-array transducers can be modeled accurately and efficiently using arrays of point sources. This approach is now extended to point sources/receivers, which allows for simulation of the transmit/receive fields, and thus the physical processes underlying ultrasound image formation. Field distributions and fast-time signals are shown to compare favorably to those obtained using the impulse response method. Doppler spectrogram and B-mode images derived from these signals also show excellent agreement with the results obtained using the impulse response method, but with a computational savings of nearly two orders of magnitude. Because of the inherent simplicity of our Fast and Mechanistic Ultrasound Simulation (FAMUS) approach, CPU parallelization was readily achieved, and further orders of magnitude speed improvements, and thus real-time performance, can be anticipated via extension to modern graphics processing units.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".