FAMUS II: A Fast and Mechanistic Ultrasound Simulator Using an Impulse Response Approach
Bibliographic record
Abstract
Real-time simulation of ultrasound images is increasingly important for providing a means of presenting a wide variety of clinical images for the training of ultrasound specialists and technologists. In order to realistically represent the visual effects caused by changes to the transducer position or its focal properties, very rapid transducer field response calculations are needed, typically on the order of a fraction of a second. Currently available methods are severely limited in this regard. Based on the impulse response, a point source/receiver method for accurately calculating the fields produced by ultrasound transducer arrays is proposed and illustrated with realistic B-mode and Doppler spectral display simulations. The results of this method (FAMUS II), which accounts for the attenuation frequency dependence of the propagating medium, are compared with those obtained with Field II both in terms of quality and computational speed. From a clinical simulation perspective, the qualitative differences are small. Because the method is inherently parallelizable, significant gains in computational speed can be achieved. For example, in B-mode imaging using an eight-core CPU, FAMUS II is shown to be more than two orders of magnitude faster than that achieved by Field II. As a result, we believe that this new method represents a significant step toward achieving real-time performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".