Resolution enhancement in laser scanning microscopy with deconvolution switching laser modes (D-SLAM)
Bibliographic record
Abstract
Laser scanning microscopy is limited in lateral resolution by the diffraction of light. Superresolution methods have been developed since the 90s to overcome this limitation. However superresolution is generally achieved at the expense of a greater complexity (high power lasers, very long acquisition times, specific fluorophores) and limitations on the observable samples. In this paper we propose a method to improve the resolution of confocal microscopy by combining different laser modes and deconvolution. Two images of the same field are acquired with the confocal microscope using different laser modes and used as inputs to a deconvolution algorithm. The two laser modes have different Point Spread Functions and thus provide complementary information leading to an image with enhanced resolution compared to using a single confocal image as input to the same deconvolution algorithm. By changing the laser modes to Bessel-Gauss beams we were able to further improve the efficiency of the deconvolution algorithm and obtain images with a residual Point Spread Function having a width of 0.14 λ (72 nm at a wavelength of 532 nm). This method only requires a laser scanning microscope and is not dependent on certain specific properties of fluorescent proteins. The proposed method requires only a few add-ons to classical confocal or two-photon microscopes and can easily be retrofitted into an existing commercial laser scanning microscope.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".