Deep non-contact photoacoustic initial pressure imaging
Bibliographic record
Abstract
Photoacoustic imaging techniques have been extensively developed for biomedical applications, including functional and molecular imaging, due in part to their high optical contrast, high spatial resolution, and non-ionizing imaging properties. However, there are currently depth limitations in cellular-resolution, optically focused photoacoustic microscopy systems. In addition, most common photoacoustic systems need to be in contact with the sample through an ultrasound medium. In this work, by taking advantage of large photoacoustic initial pressures, all-optical non-contact optical resolution photoacoustic imaging is reported at depths beyond the optical transport mean-free path of the excitation wavelength. The proposed technique is called deep photoacoustic remote sensing (dPARS) microscopy. Visible pulsed excitation wavelengths are used to produce large initial-pressure-induced refractive index modulations in absorbing targets. These localized pressure rises create transient variations to the local scattering properties, which are detected as back-reflected intensity modulations from a deep-penetrating interrogation beam and do not require an interferometric detection pathway. Experiments demonstrate that dPARS is capable of providing optical resolution images to depths of 2.5 mm in tissue-mimicking scattering media. Signal-to-noise ratio ∼50 dB is reported for in vivo imaging of microvascular networks. Also, imaging of single red blood cells, oxygen saturation mapping, and deep-vascular imaging applications are demonstrated. dPARS’s capabilities such as remote sensing, deep optical resolution imaging, and high signal-to-noise ratio, may yield new opportunities for several pre-clinical and clinical applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".