Oscillating gradient spin‐echo (OGSE) diffusion tensor imaging of the human brain
Bibliographic record
Abstract
PURPOSE: The dependence of diffusion tensor imaging (DTI) eigenvalues and fractional anisotropy (FA) on short diffusion times was investigated using oscillating gradient spin echo (OGSE) and pulsed gradient spin echo (PGSE) DTI in the human brain in vivo. THEORY AND METHODS: DTI was performed in seven healthy volunteers at 4.7 Tesla (T) with b = 300 s/mm(2) and diffusion times of 4.1 ms (OGSE 50 Hz), 7.4 ms (OGSE 25 Hz), 20 ms (PGSE), and 40 ms (PGSE). Eigenvalues and FA were compared in the corpus callosum body, splenium and genu, and the corticospinal, cingulum, inferior fronto-occipital, superior and inferior longitudinal fasciculi using tractography, and the thalamus and putamen using region-of-interest. RESULTS: Relative to 40 ms, the 4.1 ms diffusion time led to significant increases in DTI eigenvalues in seven white matter tracts (6% to 20% parallel, 13% to 40% perpendicular) and both deep gray matter regions (16% parallel, 18% to 26% perpendicular), and reductions of FA (-9% to -12%) in four tracts. CONCLUSION: DTI eigenvalues and FA depend on diffusion time in both white and gray matter in the human brain. The ability to target different length scales by means of the diffusion time may improve sensitivity to changes in tissue microstructure associated with pathology.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".