Performance Characteristics of Photoacoustic Imaging Probes with Varying Frequencies and Light-delivery Schemes
Bibliographic record
Abstract
Photoacoustic imaging (PAI) is an emerging biomedical imaging technique that utilizes a combination of light and ultrasound to detect photoabsorbers embedded within tissues. While the clinical utility of PAI has been widely explored for several applications, limitations in light penetration and detector sensitivity have restricted these studies to mostly superficial sites. Given the importance of PA signal generation and detection on light delivery and ultrasound detector frequency, there is an ongoing effort to optimize these parameters to enhance photoabsorber detection at increased depths. With this in mind, in this study we examined performance benchmarks of a commercially available PAI/ultrasound linear array system when using different imaging frequencies and light delivery schemes. A modified light fiber jacket providing focused light delivery (FLD) at the center of the probe was compared with the built-in fiber optics lining the length of the probe. Studies were performed in vitro to compare performance characteristics such as imaging resolution, maximum imaging depth, and sensitivity to varying hematocrit concentration for each frequency and light delivery method. Monte Carlo simulations of each light delivery method revealed increased light penetration with FLD. In tissue-mimicking phantoms, vascular channels used to simulate blood vessels could be visualized at a depth of 2.4 cm when lowering imaging frequency and utilizing FLD. Imaging at lower frequencies with FLD also enabled enhanced detection of varying hematocrit concentration levels at increased depths, although lateral imaging resolution was reduced. Finally, a proof of concept in vivo probe comparison study in a mouse tumor model provided supportive evidence of our in vitro results. Collectively, our findings show that adjusting imaging frequency and applying FLD can be a straightforward approach for improving PAI performance.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".