Cartilage imaging of a rabbit knee using dual-energy X-ray microscopy and 1.0 T and 9.4 T magnetic resonance imaging
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: Osteoarthritis is a common chronic disease of the joints characterised by the degeneration of articular cartilages and subchondral bone. The most common diagnostic imaging used clinically is X-ray; however, it cannot directly image cartilage. Magnetic resonance imaging (MRI) is well suited for cartilage imaging, but it requires costly and lengthy scans. For preclinical work, microcomputed tomography provides high spatial resolution and contrast for bone, however, its standard application is not well suited for cartilage imaging. METHODS: We performed a preliminary investigation into the use of dual-energy X-ray microscopy (XRM) for cartilage imaging and analysis of a rabbit knee, and compared it to the MRI results from 9.4 T and 1.0 T small-animal scanners. RESULTS: The XRM images offer a higher image resolution (∼25 μm nominal isotropic resolution) compared with the MRI (50-86 μm in plane, and 250 μm slice thickness). The cartilage-thickness measurements using the dual-energy XRM are on average 3.8% (femur) and 5.1% (tibia) thicker estimates than the 9.4 T MRI results. The cartilage-thickness measurements using the 1.0 T MRI are on average 10.9% (femur) and 2.3% (tibia) thinner estimates than the 9.4 T MRI results. CONCLUSION: Our results suggest that the dual-energy XRM for articular-cartilage analysis is feasible and comparable to the MRI. This technology will provide good support for high-resolution animal-osteoarthritis studies, and in the future, it may be possible to apply dual energy in a clinical setting.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".