SU‐E‐I‐126: Feasibility of Myelin Water Fraction Quantification Using Multi‐ Component Gradient Echo Sampling of Spin Echoes
Bibliographic record
Abstract
Purpose: Multi‐component T2 is a magnetic resonance imaging technique which allows the quantification of the myelin water fraction (MWF) non invasively in the brain. The MWF is the proportion of the shorter T2 signal arising from water trapped within layers of the myelin sheath and has been shown to be closely related to aberrant white matter microstructure, such as in multiple sclerosis lesions. The purpose of this study was to evaluate the theoretical ability of a new method to calculate the MWF in a two‐ component model using simulated data and realistic temporal signal to noise (SNR) profiles. Methods: The signal is sampled at equal intervals using gradient echoes placed symmetrically about multiple spin echoes while they rephase and subsequently dephase. Considering a two component model with a bi‐exponential signal decay, a ratio of the signal before and after the spin echo can be obtained which depends on the MWF and a short and long T2 component. Using Matlab, data was generated based on the acquisition of three spin echoes at echo times of 16, 50 and 150ms respectively flanked by 8, 18 and 40 pairs of gradient echoes spaced 1 ms apart. Realistic temporal SNR profiles were generated for the case of zero static dephasing and for a worst scenario case scenario based on realistic expectations at 3T. Results: Simulation results indicated a slight overestimation of the MWF, but distributions and standard deviations were well behaved at worsening SNR levels. Conclusions: The ability of this acquisition scheme and simple model to evaluate the contribution of a short T2 component was demonstrated. Future work will focus on the practical implementation of this technique in‐vivo which would enable the assessment of white matter microstructure by providing MWF data in a clinically relevant scan time of approximately 10 to 20 minutes.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".