Fiber-based photoacoustic remote sensing microscopy and spectral-domain optical coherence tomography with a dual-function 1050-nm interrogation source
Bibliographic record
Abstract
SIGNIFICANCE: Spectral-domain optical coherence tomography (SD-OCT) offers depth-resolved imaging of optical scattering contrast but is limited in sensitivity to optical absorption. Dual-modality imaging combined with the noncontact absorption contrast of photoacoustic remote sensing (PARS) microscopy can augment SD-OCT applications with specific molecular and functional contrasts in an all-optical, fiber-based platform. AIM: To develop a fiber-based multimodal PARS and SD-OCT imaging system, which efficiently uses a common 1050-nm light source for SD-OCT and PARS interrogation. APPROACH: PARS microscopy has predominantly utilized a 1310-nm interrogation light source to date. Hence, a recent dual-modality PARS and 1050-nm SD-OCT imaging system required three distinct wavelengths including a 532-nm PARS excitation, necessitating a free-space optical architecture with discrete subsystems. Here, we validate the first use of a 1050-nm interrogation wavelength for PARS. This enables the transition to fiber-based interferometry as is standard in modern SD-OCT systems, though infeasible with inclusion of an additional 1310-nm wavelength. PARS interrogation functionality is integrated using a broadband optical circulator. RESULTS: Dual-modality imaging is demonstrated in carbon fiber phantoms and a mouse ear in vivo. SD-OCT provided a 4.5-μm lateral resolution, 8.8-μm axial resolution in air, and >101 dB of sensitivity, and PARS contributed 532-nm optical absorption contrast with a 47-dB SNR, and lateral and axial resolutions of 2.4 and 35 μm, respectively. Total interrogation power was reduced from 90% to 58% of the ANSI limit compared to a previous three-wavelength approach. CONCLUSIONS: Adapting PARS to use the 1050-nm SD-OCT light source for interrogation enabled implementation of a fiber-based dual-modality system configuration, with image quality maintained. This will facilitate development of potential applications demanding handheld, catheter-based, or endoscopic form factors.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".