Myelin water imaging: Implementation and development at 3.0T and comparison to 1.5T measurements
Bibliographic record
Abstract
Multicomponent T(2) relaxation imaging can be used to measure signal from water trapped between myelin bilayers; the ratio of myelin water signal to total water is termed the myelin water fraction (MWF). The goal of this study was to implement and develop the single-slice T(2)-imaging technique proposed by Poon and Henkelman. For refinement, scan parameters (gradient crusher height and slew rate, bandwidth, echo spacing, matrix size, repetition time, and phase rewinding) were varied in water-based phantoms and in fixed and in vivo brain. Changes in the standard deviation of the residuals of the multiexponential fit, MWF, T(2), and peak width of the intra/extracellular water were monitored to determine which scan parameters minimized artifacts. Subsequently, we compared multicomponent T(2) measurements at 1.5T and 3.0T for 10 healthy volunteers, and investigated the differences in SNR, fit residuals, MWF, and T(2) and peak width of the intra/extracellular water, at higher magnetic field. MWF maps were found to be qualitatively similar between field strengths. MWFs were found to be significantly higher at 3.0T than at 1.5T, but with a strongly significant correlation between measurements (R(2) > 0.92, P < 0.0005). The signal-to-noise ratio (SNR) was nearly double at 3.0T, but the standard deviation of residuals was increased in most cases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".