Multicenter Measurements of T<sub>1</sub> Relaxation and Diffusion Tensor Imaging: Intra and Intersite Reproducibility
Bibliographic record
Abstract
ABSTRACT BACKGROUND AND PURPOSE Quantitative T1 and diffusion tensor imaging (DTI) may provide information about pathological changes underlying disability and progression in diseases like multiple sclerosis (MS). Imaging the corpus callosum (CC), a primary site of damage in MS with a critical role in interhemispheric connectivity, may be useful for assessing overall brain health, prognosis, and therapy efficacy. We assessed the feasibility of multisite clinical trials using advanced MRI by examining the intra and intersite reproducibility of T1 and DTI measurements in the CC and segmented white matter (WM). METHODS Five healthy volunteers were scanned twice within 24 hours at six 3T sites. Coefficients of variation (COVs) and intraclass correlation coefficients (ICCs) for CC and WM T1, fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (Dax), and radial diffusivity (Drad) assessed intrasite and intersite reliability. RESULTS CC and WM T1 showed excellent intrasite reproducibility with low COVs (mean = .90% and .89%, respectively) and good ICCs (CC = .78, WM = .90). T1 also demonstrated intersite reliability (low COVs: CC = 2.4%, WM = 1.8%; moderate ICCs: CC = .43, WM = .69). DTI had low intrasite COVs (CC: FA = 1.3%, MD = 1.5%, Dax = 1.4%, Drad = 2.2%; WM: FA = .9%, MD = .9%, Dax = .7%, Drad = 1.2%) and high intrasite ICCs (CC: FA = .95, MD = .97, Dax = .94, Drad = .97; CC: FA = .9, MD = .66, Dax = .88, Drad = .63), indicating excellent intrasite reproducibility. DTI also showed excellent intersite reliability with low COVs (CC: FA = 2.1%, MD = 4.1%, Dax = 3.4%, Drad = 5.3%, WM: FA = 1.3%, MD = 1.9%, Dax = 1.8%, Drad = 2.1%,) and good ICCs (CC: FA = .90, MD = .84, Dax = .72, Drad = .90; WM: FA = .83, MD = .34, Dax = .62, Drad = .41). CONCLUSIONS T1 and DTI measures are reproducible using equivalent MRI scanners and sequence protocols. Using a similar MR system, it is feasible to carry out multicenter studies using T1 and DTI to evaluate changes within the CC and 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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".