Label-free multimodal imaging with simultaneous two-photon and three-photon microscopy and kernel-based nonlinear scaling denoising
Bibliographic record
Abstract
We report on a compact multimodal imaging system that can acquire two-photon microscopy (2PM) and three-photon microscopy (3PM) images simultaneously. With dual excitation wavelengths, multiple contrasts including two-photon-excitation-fluorescence (2PEF), second harmonic generation (SHG), and third harmonic generation (THG) are acquired simultaneously from cells, collagen fibers, and interfaces, all label-free. Challenges related to the excitation by two wavelengths and the effective separation of 2PM and 3PM signals are discussed and addressed. The data processing challenge where multiple contrasts can have significantly varying signal levels is also addressed. A kernel-based nonlinear scaling (KNS) denoising method is introduced to reduce noise from ultra-low signal images and generate high-quality multimodal images. Simultaneous 2PM and 3PM imaging is demonstrated on various tissue samples. The simultaneous acquisition speeds up the imaging process and minimizes the commonly encountered problem of motion artifacts and mechanical drift in sequential acquisition. Multimodal imaging with simultaneous 2PM and 3PM will have great potential for label-free in-vivo imaging of biological tissues.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".