Correction of artefacts in optical projection tomography
Bibliographic record
Abstract
A new imaging technique called optical projection tomography (OPT), essentially an optical version of x-ray computed tomography (CT), provides molecular specificity, cellular resolution and larger specimen coverage ( approximately 1 cubic centimetre) than was previously possible with other imaging techniques. It is ideally suited to gene expression studies in small animals. Reconstructed OPT images demonstrate several artefacts which reduce the overall image quality. In this paper, we describe methods to prevent smear artefacts due to illumination intensity fluctuation, ring artefacts due to CCD pixel sensitivity variation and a new 'detector edge' artefact caused by non-zero background signal. We also present an automated method to align the position of the rotational axis during image reconstruction. Finally, we propose a method to eliminate bowl artefacts due to projection truncation using a lower resolution OPT scan of the same specimen. This solution also provides OPT with the ability to obtain a high-resolution reconstruction from a region of interest of a specimen that is larger than the field of view. Implementation of these corrections and modifications increases the accuracy of the OPT imaging technique and extends its capabilities to obtain higher resolution data from within a whole specimen.
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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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".