Bibliographic record
Abstract
Ultrasound computed tomography (USCT) aims at safe and fast high resolution imaging but due to its complexity and time consuming reconstruction procedures this imaging modality is not commercial in use.One can imagine USCT as an imaging procedure where X-rays source in a computed tomography scanner are replaced by ultrasound source, but in practice the straight ray tomographic imaging principle cannot be directly applied because ultrasound does not travel in a simple straight line alone.It undergoes diffraction due to relatively large wavelengths associated with typical ultrasound sources.USCT which considers diffraction property of tissues is said to be working in diffraction mode.In this research, we analyze Ultrasound computed tomography in diffraction mode.The wave equation is theoretically and numerically solved with Helmholtz equation using Green's function under consideration of various approximations to linearize the integral representation while considering the diffraction of wave is due to scattering terms as a function of compressibility and velocity.The received field found by solving wave equation was simulated.We also propose a new approach for reconstructing the parameters of interest.These simulation results indicate that proposed method can yield images with higher image resolution with a better computation time compared to other existing models.
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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) | 0.001 | 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) | 0.007 | 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".