Thresholding approaches for estimating paraspinal muscle fat infiltration using <scp>T1</scp> ‐ and <scp>T2</scp> ‐weighted <scp>MRI</scp> : Comparative analysis using water–fat <scp>MRI</scp>
Bibliographic record
Abstract
Abstract Background Paraspinal muscle fat infiltration is associated with spinal degeneration and low back pain, however, quantifying muscle fat using clinical magnetic resonance imaging (MRI) techniques continues to be a challenge. Advanced MRI techniques, including chemical‐shift encoding (CSE) based water–fat MRI, enable accurate measurement of muscle fat, but such techniques are not widely available in routine clinical practice. Methods To facilitate assessment of paraspinal muscle fat using clinical imaging, we compared four thresholding approaches for estimating muscle fat fraction (FF) using T1‐ and T2‐weighted images, with measurements from water–fat MRI as the ground truth: Gaussian thresholding, Otsu's method, K‐mean clustering, and quadratic discriminant analysis. Pearson's correlation coefficients ( r ), mean absolute errors, and mean bias errors were calculated for FF estimates from T1‐ and T2‐weighted MRI with water–fat MRI for the lumbar multifidus (MF), erector spinae (ES), quadratus lumborum (QL), and psoas (PS), and for all muscles combined. Results We found that for all muscles combined, FF measurements from T1‐ and T2‐weighted images were strongly positively correlated with measurements from the water–fat images for all thresholding techniques ( r = 0.70–0.86, p < 0.0001) and that variations in inter‐muscle correlation strength were much greater than variations in inter‐method correlation strength. Conclusion We conclude that muscle FF can be quantified using thresholded T1‐ and T2‐weighted MRI images with relatively low bias and absolute error in relation to water–fat MRI, particularly in the MF and ES, and the choice of thresholding technique should depend on the muscle and clinical MRI sequence of interest.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".