Clinical translation of photoacoustic imaging using exogenous molecular contrast agents [Invited]
Bibliographic record
Abstract
Photoacoustic imaging (PAI) combines optical contrast with acoustic detection to enable high-resolution, molecular imaging at clinically relevant depths. This review outlines the current status and future potential of contrast-enhanced PAI in human applications. We begin by discussing regulatory considerations surrounding both imaging devices and exogenous contrast agents, highlighting safety concerns, lack of standardized validation protocols, and barriers to the approval of novel agents. To accelerate clinical adoption, many studies have focused on repurposing FDA-approved agents such as indocyanine green, methylene blue, and clofazimine, which offer favorable optical properties and known safety profiles. We then review clinical applications of contrast-enhanced PAI across organ systems. In lymphatic imaging, PAI enables noninvasive visualization of lymphatic vessels and sentinel lymph nodes. Prostate imaging benefits from improved tumor delineation, and vascular applications leverage PAI to assess oxygen saturation and vascular remodeling. In gastrointestinal and hepatic imaging, PAI supports functional assessment and lesion detection with enhanced contrast. Emerging applications in neuro-oncology demonstrate the potential of PAI for intraoperative guidance and brain tumor imaging. Compared to fluorescence imaging, PAI provides deeper penetration and quantifiable contrast. Studies using both approved and investigational agents, including gold nanorods and targeted dye conjugates, highlight advances in imaging tumor margins. Progress in transcranial PAI and molecular probe design continues to broaden its capabilities. Together, these developments underscore the expanding clinical utility of contrast-enhanced PAI for real-time, functional, and molecular 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 | 0.003 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".