Reproducibility of quantitative magnetization‐transfer imaging parameters from repeated measurements
Bibliographic record
Abstract
Quantitative magnetization-transfer imaging methods provide in vivo estimates of parameters of the two-pool model for magnetization-transfer in tissue. The goal of this study was to evaluate the reproducibility of quantitative magnetization-transfer imaging parameter estimates in healthy subjects. Magnetization-transfer-weighted and T(1) relaxometry data were acquired in five healthy subjects at multiple time points, and the variability of the resulting fitted magnetization-transfer parameters was evaluated. The impact of subsampling the magnetization-transfer data and correcting field inhomogeneities was also evaluated. The key parameters measured in this study had an average variability, across time points, of 4.7% for the relative size of the restricted pool (F), 7.3% for the forward exchange constant (k(f)), 1.9% for the free pool spin-lattice relaxation constant (R(1f)), 4.5% for the T(2) of the free pool (T(2f)), and 2.3% for the T(2) of the restricted pool (T(2r)). Our findings show that serial quantitative magnetization-transfer imaging experiments can be performed reliably, with good reproducibility of the model parameter estimates, and demonstrate the reproducibility of acquisition schemes with fewer magnetization-transfer contrasts. This establishes the feasibility of this technique for monitoring patients affected by degenerative white matter diseases while providing critical data to estimate the statistical power of such studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".