Intersession Repeatability of <scp>Diffusion‐Tensor</scp> Imaging in the Supraspinatus and the Infraspinatus Muscles of Volunteers
Bibliographic record
Abstract
Background Quantifying the rotator cuff (RC) muscles' viscoelasticity could provide outcome relevant information in patients with RC tears. MR‐elastography requires robust diffusion‐tensor imaging (DTI) to account for tissue anisotropy in muscles stiffness computation. Purpose To assess the repeatability of DTI parameters in the supraspinatus and infraspinatus muscles and to explore DTI tractography conformity with the muscles' anatomy. Study Type Prospective. Subjects Six healthy volunteers underwent three consecutive shoulder MRI sessions about 10 minutes apart. Field Strength/Sequence 3T/T1‐vibe Dixon and Spin echo EPI DTI (12 gradient encoding directions, b‐values 500 and 800 sec/mm2). Assessment Supraspinatus and infraspinatus muscles were segmented on the T1‐vibe Dixon sequence. DTI image quality was assessed using a quantitative threshold based on the signal‐to‐noise ratio (SNR). The eigenvalues (), fractional anisotropy (FA) and mean diffusivity were calculated. DTI tractography was visually assessed. Statistical Tests DTI parameters within‐subject intersession repeatability was assessed with Bland–Altman analysis and the coefficient of variation (CV). Repeatability was considered good for CV < 10%. Results The SNR between diffusion‐weighted and non‐diffusion‐weighted images was greater than 3, which aligns with standards for estimating DTI parameters. The FA showed the lowest mean bias (−0.007; 95% confidence interval [CI] −0.031 to 0.018) whereas the λ1 had the highest mean bias (0.146 × 10−3 mm2/sec; CI −0.034 to 0.326 × 10−3 mm2/sec). CVs of the DTI parameters varied between 3.5% (FA) and 8.4% (λ3) for the supraspinatus and between 3.2% (λ1) and 6.8% (λ3) for the infraspinatus. Tractography provided muscle fiber representations in three‐dimensional space concordant with RC anatomy. Data Conclusion DTI of the supraspinatus and infraspinatus muscles achieved an adequate SNR, allowing the measurement of the DTI metrics with good repeatability, and thus can be used for optimizing stiffness estimation in these anisotropic tissues. Evidence Level 2 Technical Efficacy Stage 2
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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 | 0.008 |
| 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 | 0.000 |
| 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".