Enhancement of optical resolution in three-dimensional refractive-index tomograms of biological samples by employing micromirror-embedded coverslips
Bibliographic record
Abstract
Optical diffraction tomography (ODT) enables the reconstruction of the three-dimensional (3D) refractive-index (RI) distribution of a biological cell, which provides invaluable information for cellular and subcellular structures in a non-invasive manner. However, ODT suffers from an inferior axial resolution, due to the limited accessible angles imposed by the numerical aperture of the objective lens. In this study, we propose and experimentally demonstrate an approach to enhance the 3D reconstruction performance in ODT. By employing trapezoidal micromirrors, side scattered signals from the sample are measured for various side plane-wave-illumination angles. By combining the side scattered fields with the forward scattered fields, the axial resolution and 3D image quality of ODT are improved, without changing optical instruments. The feasibility and applicability of the proposed method are demonstrated by reconstructing the 3D RI distribution of a red blood cell and HeLa cells in hydrogel. We also present systematic analyses of the improved 3D imaging performance using numerical simulations and experimental measurements for the 3D transfer function, a point object, and a microsphere. The analyses demonstrate an improved axial resolution of 0.31 μm, 4.8 times smaller than that of the conventional method. The proposed method enables the non-invasive and accurate 3D imaging of 3D cultured cells, which is crucial for cell biology studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.000 | 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 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".