Enhancement of photoacoustic tomography in the tissue with speed-of-sound variance using ultrasound computed tomography
Bibliographic record
Abstract
The speed-of-sound variance will decrease the imaging quality of photoacoustic tomography in acoustically inhomogeneous tissue. In this study, ultrasound computed tomography is combined with photoacoustic tomography to enhance the photoacoustic tomography in this situation. The speed-of-sound information is recovered by ultrasound computed tomography. Then, an improved delay-and-sum method is used to reconstruct the image from the photoacoustic signals. The simulation results validate that the proposed method can obtain a better photoacoustic tomography than the conventional method when the speed-of-sound variance is increased. In addition, the influences of the speed-of-sound variance and the fan-angle on the image quality are quantitatively explored to optimize the image scheme. The proposed method has a good performance even when the speed-of-sound variance reaches 14.2%. Furthermore, an optimized fan angle is revealed, which can keep the good image quality with a low cost of hardware. This study has a potential value in extending the biomedical application of photoacoustic tomography.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".