Diffusion-Tensor Imaging at 3 T
Bibliographic record
Abstract
OBJECTIVE: Fractional anisotropy (FA) is a powerful measure to study the integrity of the cerebral white matter in vivo. However, because clinical FA assessments are frequently based on single slice evaluations, intra- and interindividual comparisons are highly dependent on image alignment. We attempted to develop an observer-independent, fully automated technique for quantitative FA assessment. MATERIALS AND METHODS: We employed whole brain diffusion tensor imaging at 3 T with an echo planar imaging sequence (isotropic spatial resolution 1.8 mm) on 4 patients (2x Alzheimer disease, 1x microangiopathy, 1x paraneoplastic disease) and 2 normal control groups (group "young," age 19-32 years; group "old," age 59-69 years). The images were spatially normalized to the standard brain template of the Montreal Neurologic Institute. We introduced a fractional anisotropy index (FAI) as a single measure for the mean tissue anisotropy in certain brain regions of interest. The regions of interest were defined by masks in relation to the Montreal Neurologic Institute coordinate space. We varied the spatial extent of the masks. Confidence intervals of the FAIs for both control groups were calculated. RESULTS: We found the resulting FAIs to be highly robust against considerable mask variations (product-moment correlation: r > 0.97). The FAIs of the 4 patients presented with neurologic conditions associated with white matter alterations significantly fell outside the confidence intervals for normal FA. CONCLUSION: FAIs based on mean fractional anisotropy values obtained from isotropic whole-head high-field diffusion tensor imaging by fully automated algorithms represent a robust and observer-independent measure for the comparative assessment of white matter integrity, ideally suited for further statistical treatments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.008 | 0.004 |
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".