Dual-encoded magnetization transfer and diffusion imaging and its application to tract-specific microstructure mapping
Bibliographic record
Abstract
We present a novel dual-encoded magnetization transfer (MT) and diffusion-weighted sequence and demonstrate its potential to resolve distinct properties of white matter fiber tracts at the sub-voxel level. The sequence was designed and optimized for maximal MT ratio (MTR) efficiency. The resulting whole brain 2.6 mm isotropic protocol to measure tract-specific MTR has a scan time under 7 minutes. Ten healthy subjects were scanned twice to assess repeatability. Two different analysis methods were contrasted: a technique to extract tract-specific MTR using Convex Optimization Modeling for Microstructure Informed Tractography (COMMIT), a global optimization technique; and conventional MTR tractometry. The results demonstrate that the tract-specific method can reliably resolve the MT ratios of major white matter fiber pathways and is less affected by partial volume effects than conventional multi-modal tractometry. By reducing the contamination due to partial volume averaging of tracts, dual-encoded MT and diffusion may increase the sensitivity to microstructure alterations of specific tracts due to disease, aging, or learning, as well as lead to weighted structural connectomes with more anatomical specificity.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".