Imaging articular cartilage under compression—cartilage elastography
Bibliographic record
Abstract
We constructed a device to compress small samples of articular cartilage while the samples were imaged in a 1.5 T imager. With the use of a piezoelectric piston, the device compressed 1-cm-diameter cylindrical samples of articular cartilage (200 microm) at a rate of 2 Hz. Simultaneously, we imaged the samples with a displacement-sensitive stimulated-echo acquisition mode (STEAM) sequence. We validated the technique using tissue that mimicked silicone samples. We compared the results from the same cartilage samples before and after they were degraded by digestion in trypsin. The extent of degradation was visualized from T(1)-weighted images of the samples after they were soaked in 0.5 mmolar of GdDTPA. The resulting elastographic images show compression and differential strain in directions both parallel and perpendicular to the surface of the cartilage. The static elastographic images that depict compression made before digestion and after 5 and 15 hr of trypsin digestion show that the elastic modulus of the samples decreased with a spatial variation consistent with the enzymatic digestion as revealed by the T(1) images. We believe this technique will be useful in studies of the mechanical properties of articular cartilage and other tissues, and may in the future be extended to the 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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".