Fast volumetric imaging with line-scan confocal microscopy by electrically tunable lens at resonant frequency
Bibliographic record
Abstract
In microscopic imaging of biological tissues, particularly real-time visualization of neuronal activities, rapid acquisition of volumetric images poses a prominent challenge. Typically, two-dimensional (2D) microscopy can be devised into an imaging system with 3D capability using any varifocal lens. Despite the conceptual simplicity, such an upgrade yet requires additional, complicated device components and usually suffers from a reduced acquisition rate, which is critical to properly document rapid neurophysiological dynamics. In this study, we implemented an electrically tunable lens (ETL) in the line-scan confocal microscopy (LSCM), enabling the volumetric acquisition at the rate of 20 frames per second with a maximum volume of interest of 315 × 315 × 80 µm3. The axial extent of point-spread-function (PSF) was 17.6 ± 1.6 µm and 90.4 ± 2.1 µm with the ETL operating in either stationary or resonant mode, respectively, revealing significant depth axial penetration by the resonant mode ETL microscopy. We further demonstrated the utilities of the ETL system by volume imaging of both cleared mouse brain ex vivo samples and in vivo brains. The current study showed a successful application of resonant ETL for constructing a high-performance 3D axially scanning LSCM (asLSCM) system. Such advances in rapid volumetric imaging would significantly enhance our understanding of various dynamic biological processes.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".