Interlaced realtime channel-domain photoacoustic and ultrasound imaging
Bibliographic record
Abstract
Photoacoustic imaging offers a new and complementary contrast mechanism to the traditional structural contrast of ultrasound. While the combination of these two modes has been demonstrated in the past with single-element transducers, array transducers offer clear advantages in both modes by eliminating mechanical scanning and allowing image formation from a single excitation. Given the abundance of commercially available ultrasound systems, it is desirable to use them as much as possible. However, these systems often only allow access to beamformed RF data. We discuss the applicability of ultrasound beamformers for photoacoustic imaging, and find that with only software-defined control over the speed of sound, walking aperture reconstruction is optimally performed using a speed correction factor of 1.414. When sector-scanning is used, a different strategy is required. We also demonstrate a new photoacoustic-ultrasound imaging system based on a Verasonics ultrasound array system. The system streams raw channel data to a 6-core PC at up to 1.4GB/s via PCI-Express, allowing interlaced ultrasound and photoacoustic data to be acquired and reconstructed at realtime rates. Using an L7-4 linear array transducer, we demonstrate the performance of this system and discuss potential applications. The system should provide new opportunities for clinical and pre-clinical 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) | 0.000 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".