Multiple Sclerosis: Validation of MR Imaging for Quantification and Detection of Iron
Bibliographic record
Abstract
PURPOSE: To investigate the relationship between iron staining and magnetic resonance (MR) imaging measurements in postmortem subjects with multiple sclerosis (MS). MATERIALS AND METHODS: Institutional ethical approval was obtained, and informed consent was obtained from the subjects and/or their families. Four MR imaging methods based on transverse relaxation (T2 weighting, R2 mapping, and R2* mapping) and phase imaging were performed by using a 4.7-T system in three in situ postmortem patients with MS less than 28 hours after death and in one in vivo patient 1 year before death. Iron staining with the Perls iron reaction was performed after brain extraction. Region-of-interest measurements from six subcortical gray matter structures were obtained from MR imaging and then correlated with corresponding locations on photographs of iron-stained pathologic slices by using a separate linear least-squares regression in each subject. Iron status of white matter lesions, as determined by staining, was compared with appearance on MR images. RESULTS: R2* mapping had the highest intrasubject correlations with iron in subcortical gray matter (R(2) = 0.857, 0.628, and 0.685; all P < .001), while R2 mapping (R(2) = 0.807, 0.615, 0.628, and 0.489; P < .001 and P = .001, .034, and .001, respectively), phase imaging (R(2) = 0.672, 0.441, 0.596, 0.548; all P ≤ .001), and T2-weighted imaging (R(2) = 0.463, 0.582, 0.650, and 0.551; all P < .001) had lower but still strong correlations. Within lesions, hypointense areas on phase images did not always represent iron. A hyperintense rim surrounding lesions on R2* maps was only present with iron staining, yet not all iron-staining lesions had R2* rim hyperintensity. CONCLUSION: All four MR imaging methods had significant linear correlations with iron and could potentially be used to determine iron status of subcortical gray matter structures in MS, with R2* mapping being preferred. A reliable method of determining iron status within MS lesions was not established.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".