High frequency optoacoustic microscopy
Bibliographic record
Abstract
Photoacoustic imaging--also called optoacoustic imaging--is a new hybrid modality of high tissue contrast which is based on the varying optical properties of tissue. The acoustic signal generated by pulsed laser absorption reports tissue-specific information with high spatial resolution. To increase the intrinsic contrast in tissue, absorbing particles are of great interest for optical imaging because of their considerable capacity to absorb and scatter light at visible and near-infrared wavelengths. The aim of the work presented here is to establish a scalable photoacoustic technology for volume imaging of biological samples down to diffraction limited microscopy. For this purpose a versatile photoacoustic microscopy platform has been developed with unmatched spatial resolution consisting of a microchip laser and a measurement cell with different transducers attached allowing generation and detection of laser-induced ultrasound signals in a frequency range up to 400 MHz. The performance of a versatile photoacoustic microscopy platform was evaluated via 2D optoacoustic images of light absorbing microparticles (5 microm Fe(3)0(4) and 1 micromblack toner particles) embedded in a polystyrene matrix. High frequency signals in the frequency range of 400 MHz generated by a single 1 microm particle could be recorded with a high signal to noise ratio (SNR) of 34 dB.
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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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".