Distributed three-dimensional simulation of B-mode ultrasound imaging using a first-order<i>k</i>-space method
Bibliographic record
Abstract
Computational modeling is an important tool in ultrasound imaging research, but realistic three-dimensional (3D) simulations can exceed the capabilities of serial computers. This paper uses a 3D simulator based on a k-space method that incorporates relaxation absorption and nonreflecting boundary conditions. The simulator, which runs on computer clusters, computes the propagation of a single wavefront. In this paper, an allocation algorithm is introduced to assign each scan line to a group of nodes and use multiple groups to compute independent lines concurrently. The computational complexity required for realistic simulations is analyzed using example calculations of ultrasonic propagation and attenuation in the 30-50 MHz band. Parallel efficiency for B-mode imaging simulations is evaluated for various numbers of scan lines and cluster nodes. An aperture-projection technique is introduced to simulate imaging with a focused transducer using reduced computation grids. This technique is employed to synthesize B-mode images that show realistic 3D refraction artifacts. Parallel computing using 20 nodes to compute groups of ten scan lines concurrently reduced the execution time for each image to 18.6 h, compared to a serial execution time of 357.5 h. The results demonstrate that fully 3D imaging simulations are practical using contemporary computing technology.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".