Investigating the stability of mcDESPOT myelin water fraction values derived using a stochastic region contraction approach
Bibliographic record
Abstract
PURPOSE: Multicomponent driven equilibrium single pulse observation of T1 and T2 (mcDESPOT) is an alternative to established multiecho T2 -based approaches for quantifying myelin water fraction, affording increased volumetric coverage and spatial resolution. A concern with mcDESPOT, however, is the large number of model parameters that must be estimated, which may lead to nonunique solutions and sensitivity to fitting constraints. Here we explore mcDESPOT performance under different experimental conditions to better understand the method's sensitivity and reliability. METHODS: To obtain parameter estimates, mcDESPOT uses a stochastic region contraction (SRC) approach to iteratively contract a predefined solution search-space around a global optimum. The sensitivity of mcDESPOT estimates to SRC boundary conditions, and tissue parameters, was examined using numerical phantoms and acquired in vivo human data. RESULTS: The SRC approach is described and shown to return robust myelin water estimates in both numerical phantoms and in vivo data under a range of experimental conditions. However, care must be taken in choosing the initial SRC boundary conditions, ensuring they are broad enough to encompass the "true" solution. CONCLUSION: Results suggest that under the range of conditions examined, mcDESPOT can provide stabile and precise values.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".