<scp>MR</scp> Susceptibility Separation for Quantifying Lesion Paramagnetic and Diamagnetic Evolution in <scp>Relapsing–Remitting</scp> Multiple Sclerosis
Bibliographic record
Abstract
Background Multiple sclerosis (MS) lesion evolution may involve changes in diamagnetic myelin and paramagnetic iron. Conventional quantitative susceptibility mapping (QSM) can provide net susceptibility distribution, but not the discrete paramagnetic and diamagnetic components. Purpose To apply susceptibility separation (χ separation) to follow lesion evolution in MS with comparison to R2*/R2′/QSM. Study Type Longitudinal, prospective. Subjects Twenty relapsing–remitting MS subjects (mean age: 42.5 ± 9.4 years, 13 females; mean years of symptoms: 4.3 ± 1.4 years). Field Strength/Sequence Three‐dimensional multiple echo gradient echo (QSM and R2* mapping), two‐dimensional dual echo fast spin echo (R2 mapping), T2‐weighted fluid attenuated inversion recovery, and T1‐weighted magnetization prepared gradient echo sequences at 3 T. Assessment Data were analyzed from two scans separated by a mean interval of 14.4 ± 2.0 months. White matter lesions on fluid‐attenuated inversion recovery were defined by an automatic pipeline, then manually refined (by ZZ/AHW, 3/25 years' experience in MRI), and verified by a radiologist (MN, 25 years' experience in MS). Susceptibility separation yielded the paramagnetic and diamagnetic susceptibility content of each voxel. Lesions were classified into four groups based on the variation of QSM/R2* or separated into positive/negative components from χ separation. Statistical Tests Two‐sample paired t tests for assessment of longitudinal differences. Spearman correlation coefficients to assess associations between χ separation and R2*/R2′/QSM. Significant level: P < 0.005. Results A total of 183 lesions were quantified. Categorizing lesions into groups based on χ separation demonstrated significant annual changes in QSM//R2*/R2′. When lesions were grouped based on changes in QSM and R2*, both changing in unison yielded a significant dominant paramagnetic variation and both opposing yielded a dominant diamagnetic variation. Significant Spearman correlation coefficients were found between susceptibility‐sensitive MRI indices and χ separation. Data Conclusion Susceptibility separation changes in MS lesions may distinguish and quantify paramagnetic and diamagnetic evolution, potentially providing additional insight compared to R2* and QSM alone. Level of Evidence 2 Technical Efficacy Stage 2
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".