High-fidelity microsecond-scale cellular imaging using two-axis compressed streak imaging fluorescence microscopy.
Bibliographic record
Abstract
Compressed streak imaging (CSI) is a computational imaging strategy that can acquire video at over 150 trillion frames per second. Despite this achievement, CSI faces challenges in detecting subtle intensity fluctuations in slow-moving, continuously illuminated objects. This limitation, largely attributable to high streak compression and motion blur, has curtailed the broader adoption of CSI in cellular fluorescence microscopy. To address these issues and expand the utility of CSI, we developed a two-axis compressed streak imaging (TACSI) method that results in significant improvements to the reconstructed video fidelity. TACSI introduces a second scanning axis which shuttles a conjugate image of the object with respect to the coded aperture. The moving image decreases the streak compression ratio and produces a "flash and shutter" phenomenon that reduces coded aperture motion blur, overcoming the limitations of current CSI technologies. This approach is supported with an analytical model describing the TACSI compression ratio, along with simulated and empirical measurements. We demonstrate TACSI's ability to measure rapid variations in cell membrane potentials, previously unattainable with conventional CSI. This method has broad implications for high-speed photography, including visualization of action potentials, muscle contractions, and enzymatic reactions that occur on microsecond and faster timescales using fluorescence microscopy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".