Femtosecond photoacoustics: integrated two-photon fluorescence and photoacoustic microscopy
Bibliographic record
Abstract
Conventional photoacoustic imaging systems excite a photoacoustic wave by illuminating an area on the order of square centimeters with millijoule laser pulses. Spatial resolution is then determined by the ultrasound transducer and is typically on the order of 100 μm. We report on a system that focuses femtosecond, nanojoule pulses to a spot with a diameter of ~ 1 μm to perform laser-scanning photoacoustics with micrometer resolution. Near-infrared femtosecond laser pulses with a pulse energy of 2.4 nanojoules excite a train of photoacoustic waves at the repetition rate of the pulsed laser (80 MHz). These photoacoustic waves are detected by an unfocused single-element ultrasound transducer tuned to 80 MHz. A radiofrequency lock-in amplifier recovers the amplitude of the frequency component of the photoacoustic signal at the pulse repetition frequency. This amplitude is an indicator of the absorption coefficient of the sample at the laser focus and at the laser wavelength. Initial experiments using a graphite rod as absorber reproducibly yield signals in the 0.2 - 2 microvolt range with a signal-to-noise ratio of 18 dB, recovered from 10 mV of broadband noise. The photoacoustic imaging system is integrated in a commercial laser-scanning two-photon fluorescence microscope, enabling simultaneous three-dimensional fluorescence- and photoacoustic imaging. One major application will be to image both morphology and oxygen saturation of microvasculature in the cerebral cortex of anesthetized rodents in vivo in the context of tumor angiogenesis. In this paper we describe the physics of femtosecond photoacoustics and demonstrate initial results.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".