A Multivariate Approach for Denoising of T2 Relaxation Decay Curves in Myelin Water Fraction Imaging
Bibliographic record
Abstract
Accurately measuring brain myelin content in vivo holds great promise for studying and understanding of number of brain diseases. A surrogate marker for myelin, myelin water fraction (MWF), can be measured with MR relaxation techniques that sample the T2 decay with multiple echoes. Low signalto-noise ratios in later echoes, sub-optimal flip angles, and the fact that robust decomposition into multiple exponential curves is notoriously difficult, all conspire to reduce the accuracy of MWF estimates. The resulting maps are typically spatially noisy - despite the fact that adjacent white matter voxels are usually assumed to have similar myelin measures in vivo.Here we propose a spatio-temporal filtering process prior to the standard fitting based on a combination of multivariate empirical mode decomposition (MEMD) and multiset canonical correlation analysis (MCCA) to decompose and find the most robust temporal decay pattern among voxels that have similar overall decay curves and across the white matter. Based on enhanced spatial smoothness measures, increased test-retest reliability within subjects, and decreased Coefficient of Variation of MWF scores, we suggest that the proposed approach provides enhanced accuracy of the ultimately-computed MWF maps.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".