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 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 | 0.023 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".