Enhanced µCT Imaging Protocol to Enable High-Resolution 3D Visualization of Microdamage in Rat Vertebrae
Bibliographic record
Abstract
Contrast-enhanced μCT imaging has been used to provide non-destructive 3D images of microdamage, but at a lower quality than found in histology and 2D backscatter electron (BSE) imaging. This study aimed to quantify potential improvements in microdamage characterization by enhancing µCT scanning parameters. Eleven slides from 9 rat vertebrae (healthy = 3, osteolytic metastases = 3, mixed metastases = 3) previously stained for microdamage with BaSO4 and analyzed with BSE imaging (2μm voxel spacing) were used in this study. μCT imaging conducted under varying protocols (x-ray voltage, tube current, frame averaging) demonstrated enhanced scan parameters at 90 kVp, 44 µA, 0.5 mm aluminum filter, 8 times frame averaging, and 4.9 µm voxel spacing. Post-processing with Richardson-Lucy deconvolution further deblurred the μCT images. Labeled microdamage in the baseline, enhanced and deblurred μCT images were segmented and spatially quantified vs. BSE-labeled microdamage using a probability-based correlation metric at six inflation radii. Enhanced μCT scan parameters improved damage visualization and increased spatial correlation probability with BSE images. Deblurring improved the sharpness of stain boundaries but did not significantly improve spatial correlation probabilities in comparison to the enhanced scans. This enhanced μCT protocol facilitates 3D visualization of microdamage, an indicator of bone quality important to bone damage mechanics.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".