Non‐negative least squares computation for in vivo myelin mapping using simulated multi‐echo spin‐echo <i>T</i> <sub>2</sub> decay data
Bibliographic record
Abstract
Multi‐compartment T 2 mapping has gained particular relevance for the study of myelin water in the brain. As a facilitator of rapid saltatory axonal signal transmission, myelin is a cornerstone indicator of white matter development and function. Regularized non‐negative least squares fitting of multi‐echo T 2 data has been widely employed for the computation of the myelin water fraction (MWF), and the obtained MWF maps have been histopathologically validated. MWF measurements depend upon the quality of the data acquisition, B 1 + homogeneity and a range of fitting parameters. In this special issue article, we discuss the relevance of these factors for the accurate computation of multi‐compartment T 2 and MWF maps. We generated multi‐echo spin‐echo T 2 decay curves following the Carr‐Purcell‐Meiboom‐Gill approach for various myelin concentrations and myelin T 2 scenarios by simulating the evolution of the magnetization vector between echoes based on the Bloch equations. We demonstrated that noise and imperfect refocusing flip angles yield systematic underestimations in MWF and intra−/extracellular water geometric mean T 2 (gmT 2 ). MWF estimates were more stable than myelin water gmT 2 time across different settings of the T 2 analysis. We observed that the lower limit of the T 2 distribution grid should be slightly shorter than TE 1 . Both TE 1 and the acquisition echo spacing also have to be sufficiently short to capture the rapidly decaying myelin water T 2 signal. Among all parameters of interest, the estimated MWF and intra−/extracellular water gmT 2 differed by approximately 0.13–4 percentage points and 3–4 ms, respectively, from the true values, with larger deviations observed in the presence of greater B 1 + inhomogeneities and at lower signal‐to‐noise ratio. Tailoring acquisition strategies may allow us to better characterize the T 2 distribution, including the myelin water, in vivo.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".