Broadening the detection view of 2D photoacoustic tomography using two linear array transducers
Bibliographic record
Abstract
Although commercial linear array transducers are widely used in clinical ultrasound, their application in photoacoustic tomography (PAT) is still limited due to the limited-view problem that restricts the image quality. In this paper, we propose a simple approach to address the limited-view problem in 2D by using two linear array transducers to receive PAT signal from different orientations. The positions of the two transducers can be adjusted to fit the specific geometry of an imaging site. This approach is made possible by using a new calibration method, where the relative position between the two transducers can be calibrated using ultrasound by transmitting ultrasound wave with one transducer while receiving with the other. The calibration results are then applied in the subsequent PAT imaging to incorporate the detected acoustic signals from both transducers and thereby increase the detection view. In this calibration method, no calibration phantom is required which largely simplifies and shortens the process. The efficacy of the calibration and improvement on the PAT image quality are demonstrated through phantom studies and in vivo imaging.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".