Fast B-Mode Ultrasound Image Simulation of Deformed Tissue
Bibliographic record
Abstract
This paper presents a fast image synthesis procedure inside elastic volumes under deformation simulated by the finite element method (FEM). Given the node displacements of a mesh and the 3D image voxel data of a volume prior to deformation, the method maps the image pixels, to be synthesized, from the deformed configuration back to the nominal pre-deformed configuration, where the pixel intensities are obtained easily through interpolation in the regular-grid structure of the voxel volume. This mapping requires the identification of the mesh element enclosing each image pixel, in order to use its corresponding shape function for smooth interpolation. To accelerate this point location operation, a fast method of marking the projection of the deformed mesh on the image pixels at every frame is introduced. In order to evaluate our method, a deformable tissue phantom was constructed and its 3D ultrasound volume was acquired in its nominal state. B-mode images of the phantom were then synthesized under the simulated deformation of an ultrasound probe. Results show that realistic B-mode images can be simulated in real-time with the proposed technique, even under large deformations. The technique is also implemented on a real-time system for ultrasound exploration with deformation.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".