Assessment of Mechanical Properties of Muscles from Multi-Parametric Magnetic Resonance Imaging
Bibliographic record
Abstract
We hypothesized that a relationship existed between the mechanical properties and the magnetic resonance imaging (MRI) parameters of muscles, as already demonstrated in cartilaginous tissues. The aim was to develop an indirect evaluation tool of the mechanical properties of degenerated muscles. Leg and arm muscles of adult rabbits were dissected, and tested 12 hours post mortem, in a state of rigor mortis, or 72 hours post mortem, in a state of post-rigor mortis. The tests consisted of a multi-parametric MRI acquisition followed by a uniaxial tensile test until failure. The statistical analysis consisted of multiple linear regressions and principal component analysis. Significant differences existed between the rigor mortis and post-rigor mortis groups for E but not for the MRI parameters. 78%, 60% or 33% of the Young’s modulus could be explained by the MRI parameters in the post-rigor mortis group, rigor mortis group or both groups respectively. These relationships were confirmed by the principal component analysis. The proposed multi-parametric MRI protocol associated to principal component analysis is a promising tool for the indirect evaluation of muscle mechanical properties and should be useful to find biomarkers and predictive factors of the evolution of the pathologies.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".