In vivo investigation of the multi‐exponential <i>T</i><sub>2</sub> decay in human white matter at 7 T: Implications for myelin water imaging at UHF
Bibliographic record
Abstract
Introduction Multi‐component T2 mapping using a gradient‐ and spin‐echo (GraSE) acquisition has become standard for myelin water imaging at 3 T. Higher magnetic field strengths promise signal‐to‐noise ratio benefits but face specific absorption rate limits and shortened T2 times. This study investigates compartmental T2 times in vivo and addresses advantages and challenges of multi‐component T2 mapping at 7 T. Methods We acquired 3D multi‐echo GraSE data in seven healthy adults at 7 T, with three subjects also scanned at 3 T. Stimulated echoes arising from B1+ inhomogeneities were accounted for by the extended phase graph (EPG) algorithm. We used the computed T2 distributions to determine T2 times that identify different water pools and assessed signal‐to‐noise and fit‐to‐noise characteristics of the signal estimation. We compared short T2 fractions and T2 properties of the intermediate water pool at 3 T and 7 T. Results Flip angle mapping confirmed that EPG accurately determined the larger B1+ inhomogeneity at 7 T. Multi‐component T2 analysis demonstrated shortened T2 times at 7 T compared with 3 T. Fit‐to‐noise and signal‐to‐noise ratios were improved at 7 T but depended on B1+ homogeneity. Adjusting the shortest T2 to 8 ms and the T2 threshold that separates different water compartments to 20 ms yielded short T2 fractions at 7 T that conformed to 3 T data. Short T2 fractions in myelin‐rich white matter regions were lower at 7 T than at 3 T, and higher in iron‐rich structures. Discussion Adjusting the T2 compartment boundaries was required due to the shorter T2 relaxation times at 7 T. Shorter echo spacing would better sample the fast decaying signal but would increase peripheral nerve stimulation. Multi‐channel transmission will improve T2 measurements at 7 T. Conclusion We used a multi‐echo 3D GraSE sequence to characterize the multi‐exponential T2 decay at 7 T. We adapted T2 parameters for evaluation of the short T2 fraction. Obtained 7 T multi‐component T2 maps were in good agreement with 3 T data.
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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) | 0.000 | 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 | 0.001 | 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".