Normalization of White Matter Intensity on T1‐Weighted Images of Patients with Acquired Central Nervous System Demyelination
Bibliographic record
Abstract
BACKGROUND: Intensity variation between magnetic resonance images (MRI) hinders comparison of tissue intensity distributions in multicenter MRI studies of brain diseases. The available intensity normalization techniques generally work well in healthy subjects but not in the presence of pathologies that affect tissue intensity. One such disease is multiple sclerosis (MS), which is associated with lesions that prominently affect white matter (WM). OBJECTIVE: To develop a T1-weighted (T1w) image intensity normalization method that is independent of WM intensity, and to quantitatively evaluate its performance. METHODS AND SUBJECTS: We calculated median intensity of grey matter and intraconal orbital fat on T1w images. Using these two reference tissue intensities we calculated a linear normalization function and applied this to the T1w images to produce normalized T1w (NT1) images. We assessed performance of our normalization method for interscanner, interprotocol, and longitudinal normalization variability, and calculated the utility of the normalization method for lesion analyses in clinical trials. RESULTS: Statistical modeling showed marked decreases in T1w intensity differences after normalization (P < .0001). CONCLUSIONS: We developed a WM-independent T1w MRI normalization method and tested its performance. This method is suitable for longitudinal multicenter clinical studies for the assessment of the recovery or progression of disease affecting WM.
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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 | 0.002 | 0.006 |
| 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".