Resolution and contrast enhancement in laser scanning microscopy using dark beam imaging
Bibliographic record
Abstract
Laser scanning microscopy allows for three-dimensional imaging of cells with molecular specific labeling. However the spatial resolution of optical microscopy is fundamentally limited by the diffraction of light. In the last two decades many techniques have been introduced to enhance the resolution of laser scanning microscopes. However most of these techniques impose strong constraints on the specimen or rely on complex optical systems. These constraints limit the applicability of resolution improvement to various imaging modalities and sample types. To overcome these limitations, we introduce here a novel approach, which we called Switching LAser Mode (SLAM) microscopy, to enhance resolution and contrast in laser scanning microscopy. SLAM microscopy relies on subtracting images obtained with dark and bright modes, and exploits the smaller dimensions of the dark spot of the azimuthally polarized TE 01 mode. With this approach, resolution is improved by a factor of two in confocal microscopy. The technique is not based on complex nonlinear processes and thus requires laser power similar to that used in conventional imaging, minimizing photo-damage. The flexibility of the approach enables retrofitting in commercial confocal and two-photon microscopes and opens avenues for resolution enhancement in fluorescence-independent microscopy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".