Improved Photoacoustic Beamforming Utilizing Apodization Windows
Bibliographic record
Abstract
Photoacoustic (PA) imaging is a non-invasive and real-time imaging technique that is widely used in preclinical applications and is increasingly gaining acceptance in clinical settings. Known for its high spatial resolution, PA imaging enables detailed visualization of anatomical and functional information within biological tissues. However, the quality and efficiency of real-time limited-view PA imaging are often compromised by sidelobes, noise, and background signals, particularly when using delay-and-sum (DAS) beamforming with conventional ultrasound linear arrays. To address these challenges, this paper explores the adoption of appropriate receiving apodization windows to enhance PA beamforming. Both phantom and in vivo rat brain results demonstrate that selecting apodization windows with an appropriate F-number not only effectively suppresses image artifacts caused by background signals and noise, but also enhances signals from anatomical features of interest, such as the corpus callosum. The linear nature of the algorithm's operations ensures low computational complexity, allowing it to be effectively integrated into PA imaging systems and achieve real-time performance with both efficiency and image quality, ultimately contributing to more accurate quantitative analysis and diagnostic precision in medical applications, such as brain inflammation 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".