Po‐Poster ‐ 34: Diffusion tensor imaging in the human calf muscle as a measure of the muscle disorder
Bibliographic record
Abstract
The diffusion tensor imaging (DTI) is a method for the evaluation of water diffusion in organized tissues. In muscles the diffusion of water is expected to be anisotropic. It is hypothesized that diffusion is larger along muscle fibers compared to that across fibers. From a series of diffusion weighted images the diffusion tensor can be calculated. The obtained eigenvalues and eigenvectors of this tensor provide information about local muscle tissue anisotropy. The aim of this study was to determine whether DTI is a suitable method to characterize the extent of human calf muscle injuries such as tears, and the difference between immediate post‐exercise and at rest skeletal muscle. The data were collected using a GE‐3.0T shortbore scanner with a standard knee coil. Four subjects were investigated: one patient with chronic compartment syndrome affecting the posterior compartment of the calf, one patient with clinically apparent acute medial gastrocnemius tear, and two healthy volunteers. The diffusion anisotropy of water was characterized by the fractional anisotropy. The diffusion images demonstrate the anisotropy of the diffusion and the large contrast arising from the different orientations of the cells in the muscle tissue. The calculated eigenvalues of the diffusion tensor reflects the strong anisotropic character of the muscle tissue. The results of the fiber tractography method demonstrate disorder as expected for the muscle tear, while the healthy muscle shows the ordering in the fibers. As a conclusion, DTI and fibre tracking may be suitable techniques for analyzing skeletal muscle damage and evaluating lesion extent.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".