Motion free micro-endoscopic system for imaging in freely behaving animals at variable focal depths using liquid crystal lenses (Conference Presentation)
Bibliographic record
Abstract
MICRO-ENDOSCOPE : The novel micro-endoscopic system we present was designed and simulated using Zemax optical software in order to predict some key imaging parameters such as the magnification, the field of view, the resolution, the focal shift, etc. Classical epi-fluorescence (reflected light illumination) imaging configuration was considered. SolidWorks engineering software was used for the mechanical approach/simulations. The mechanical parts of the micro-endoscope were mainly printed using a 3D laser printer (hard plastic) at the theoretical resolution of 25µm or directly fabricated and assembled in the mechanical atelier. TUNEABLE LIQUID CRYSTAL LENSES : We used an optimized modal lens approach to design polarization-insensitive optical probe that requires relatively low driving voltages to perform endoscopic depth imaging. For a single LC lens a thin weakly conductive layer of ZnO film sheet resistance was cast over the hole-patterned electrode to form a the control layer used to generate a gradually varying electric field profile along the z-axis that was applied to the NLC layer. Two perpendicularly oriented double-layer NLC “half” lenses were required to form a custom four-layers design of the TLCL (tuneable liquid crystal lens). GRIN PROBE : To enable depth imaging our 4 layer TLCL was optically coupled to the imaging probe composed of 2 different GRIN lenses : imaging GRIN lens (high NA) and a coupling GRIN lens (low NA) which were glued together using index matched optical adhesive. RESULTS : Combination of the TLCL and the GRIN probe enabled a focal shift of approximately 90 ± 3µm while maintaining a constant magnification and a lateral resolution of ≈ 1µm. The potential of our system to visualise and differentiate small neuronal structures at variable focal depth was tested by imaging neurons, dendrites and also spines in thick brain sections and also in a free behaving mouse (Flex-GFP), in deep regions of the brain such as subventricular zone (SVZ) and rostral migratory stream (RMS).
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".