Efficient whole‐brain tract‐specific T<sub>1</sub> mapping at 3T with slice‐shuffled inversion‐recovery diffusion‐weighted imaging
Bibliographic record
Abstract
Purpose Most voxels in white matter contain multiple fiber populations with different orientations and levels of myelination. Conventional T1 mapping measures 1 T1 value per voxel, representing a weighted average of the multiple tract T1 times. Inversion‐recovery diffusion‐weighted imaging (IR‐DWI) allows the T1 times of multiple tracts in a voxel to be disentangled, but the scan time is prohibitively long. Recently, slice‐shuffled IR‐DWI implementations have been proposed to significantly reduce scan time. In this work, we demonstrate that we can measure tract‐specific T1 values in the whole brain using simultaneous multi‐slice slice‐shuffled IR‐DWI at 3T. Methods We perform simulations to evaluate the accuracy and precision of our crossing fiber IR‐DWI signal model for various fiber parameters. The proposed sequence and signal model are tested in a phantom consisting of crossing asparagus pieces doped with gadolinium to vary T1, and in 2 human subjects. Results Our simulations show that tract‐specific T1 times can be estimated within 5% of the nominal fiber T1 values. Tract‐specific T1 values were resolved in subvoxel 2 fiber crossings in the asparagus phantom. Tract‐specific T1 times were resolved in 2 different tract crossings in the human brain where myelination differences have previously been reported; the crossing of the cingulum and genu of the corpus callosum and the crossing of the corticospinal tract and pontine fibers. Conclusion Whole‐brain tract‐specific T1 mapping is feasible using slice‐shuffled IR‐DWI at 3T. This technique has the potential to improve the microstructural characterization of specific tracts implicated in neurodevelopment, aging, and demyelinating disorders.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".