Image registration for limited-view photoacoustic imaging using two linear array transducers
Bibliographic record
Abstract
Imaging complicated structures with photoacoustic (PA) modality when the field of view is limited can result in significant imaging artifacts or missing structures. Approaches to solve this problem include new reconstruction algorithms and specific transducer structures, such as hemi-spherical transducer arrays for breast cancer detection. However, most existing PA imaging techniques require either fullview complete projection data collection or complex and computationally-intensive reconstructions. Such approaches are not only time-consuming but also unsuitable for many clinical applications, because most clinical imaging hardware is constrained to limited reconstruction angles. In this paper, we present a method of using two commercial linear array transducers at different orientations to increase the view angle and thus improve the reconstruction of PA imaging. The method involves a two-step process. First, a calibration phantom is imaged to calibrate the relative position of these two linear transducers. Second, two PA images are obtained by a simple back projection algorithm and these images are registered using the information from the calibration process. The final registered image contains more detailed structures without the requirements of a specialized transducer or long processing time. Experimental results show that this method has the potential to provide good image quality using standard low-cost transducers.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".