Resonance-Based Frequency-Selective Amplification for Increased Photoacoustic Imaging Sensitivity
Bibliographic record
Abstract
Photoacoustic (PA) imaging has attracted much attention as a new biomedical imaging modality due to its ultrasonic spatial resolution, optical contrast resolution, and deeper imaging depth than other optical imaging modalities. Exogenous PA contrast agents have been developed, with high optical absorbance at a desired wavelength, to improve their imaging sensitivity over background signal produced from endogenous nontargeted absorbers. However, the current approaches to PA imaging are based on a nonoptimal detection of PA signal, due to the fact that the PA signal contains a broad range of frequency components, whereas an ultrasound transducer is only capable of receiving signals within a certain frequency range. As a result, much of the signal generated by PA contrast agent is lost when received by an ultrasound transducer. In this study, we propose a new concept for PA contrast enhancement. This method uses chromophore-embedded microbubbles as selective resonance frequency amplifiers; only the PA signal energy within a desired spectral bandwidth can be selectively increased by adjusting the microbubble size. Therefore, the efficiency of the signal reception by an ultrasound transducer can be improved when the operating frequency of the transducer is similar to the amplified spectral bandwidth, thus allowing for more sensitive PA imaging. This new concept was validated using a porphyrin-phospholipid microbubble (p-MB) in vitro and in vivo experiments, which showed that the p-MBs increased the PA signals up to 40.94 times, compared with the PA signals from the freely dispersed porphyrin-embedded liposomes (i.e., porphysomes).
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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 | 0.000 |
| 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 | 0.001 |
| 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".