Fast 3-D Opto-Acoustic Simulation for Linear Array With Rectangular Elements
Bibliographic record
Abstract
Simulation involves predicting the responses of a physical system. In this article, we simulate opto-acoustic signals generated in a 3-D volume due to the absorption of an optical pulse. A separable computational model is developed, which splits processing into two steps, permitting an order-of-magnitude improvement in computational efficiency over a nonseparable model. The simulated signals represent acoustic waves, measured by a probe with a linear transducer array, in a rotated and translated coordinate frame. Light is delivered by an optical source that moves with the probe's frame. Spatiotemporal impulse response for rectangular-element transducer geometry is derived using Green's function solution to the acoustic wave equation. The approach permits fast and accurate simulation for a probe with an arbitrary trajectory, which is useful for modeling freehand acquisitions. For a 3-D volume of n3voxels, computation is accelerated by a factor of n. This may potentially have application in opto-acoustic imaging, where clinicians visualize structural and functional features of biological tissue for assessment of cancer and other diseases.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".