Myelin Water Fraction and Intra/Extracellular Water Geometric Mean T<sub>2</sub>Normative Atlases for the Cervical Spinal Cord from 3T MRI
Bibliographic record
Abstract
ABSTRACT BACKGROUND AND PURPOSE Acquiring and interpreting quantitative myelin‐specific MRI data at an individual level is challenging because of technical difficulties and natural myelin variation in the population. To overcome these challenges, we used multiecho T2myelin water imaging (MWI) to create T2metric healthy population atlases that depict the mean and variation of myelin water fraction (MWF), and intra‐ and extracellular water mobility as described by geometric mean T2(IEGMT2). METHODS Cervical cord MWI was performed at 3T on 20 healthy individuals (10M/10F, mean age: 36 years) and 3 relapsing remitting multiple sclerosis (RRMS) participants (1M/2F, age: 39/42/37 years). Anatomical data were collected for the purpose of image segmentation and registration. Atlases were created by coregistering and averaging T2metrics from all controls. Voxel‐wisez‐score maps from 3 RRMS participants were produced to demonstrate the preliminary utility of the MWF and IEGMT2atlases. RESULTS The average MWF atlas provides a representation of myelin in the spinal cord consistent with well‐known spinal cord anatomical characteristics. The IEGMT2atlas also depicted structural variations in the spinal cord.Z‐score analysis illustrated distinct abnormalities in MWF and IEGMT2in the 3 RRMS cases. CONCLUSIONS Our findings highlight the potential for using a quantitative T2relaxation metric atlas to visualize and detect pathology in spinal cord. Our MWF and IEGMT2atlases (URL: https://sourceforge.net/projects/mwi-spinal-cord-atlases/ ) can serve as normative references in the cervical spinal cord for other studies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".