Assessment of the efficacy of MRI for detection of changes in bone morphology in a mouse model of bone injury
Bibliographic record
Abstract
PURPOSE: To determine whether magnetic resonance imaging (MRI) could be used to track changes in skeletal morphology during bone healing using high-resolution micro-computed tomography (μCT) as a standard. We used a mouse model of bone injury to compare μCT with MRI. MATERIALS AND METHODS: Surgery was performed to induce a burr hole fracture in the mouse tibia. A selection of biomaterials was immediately implanted into the fractures. First we optimized the imaging sequences by testing different MRI pulse sequences. Then changes in bone morphology over the course of fracture repair were assessed using in vivo MRI and μCT. Histology was performed to validate the imaging outcomes. RESULTS: The rapid acquisition with relaxation enhancement (RARE) sequence provided sufficient contrast between bone and the surrounding tissues to clearly reveal the fracture. It allowed detection of the fracture clearly 1 and 14 days postsurgery and revealed soft tissue changes that were not clear on μCT. In MRI and μCT the fracture was seen at day 1 and partial healing was detected at day 14. CONCLUSION: The RARE sequence was the most suitable for MRI bone imaging. It enabled the detection of hard and even soft tissue changes. These findings suggest that MRI could be an effective imaging modality for assessing changes in bone morphology and pathobiology.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".