Transverse relaxometry with reduced echo train lengths via stimulated echo compensation
Bibliographic record
Abstract
Transverse relaxation (T2 ) mapping has many applications, including imaging of iron accumulation in grey matter. Using the typical multiecho spin-echo sequence with long echo trains, stimulated echo compensation can enable T2 fitting under conditions of variable radio frequency homogeneity arising from slice profile and in-plane radio frequency variation. Substantial reduction in the number of refocusing pulses could enable use at high magnetic fields where specific absorption rate is a major limitation, and enable multislice use with reduced incidental magnetization transfer at all field strengths. We examine the effect of reduced echo train lengths and multislice imaging on T2 fitting using stimulated echo compensation applied to iron-rich subcortical grey matter in human brain at 4.7 T. Our findings indicate that reducing from 20 echoes to as few as four echoes can maintain consistent T2 values when using stimulated echo compensation in grey and white matter, but not for cerebrospinal fluid. All territories produce marginal results when using standard exponential fitting. Savings from reduced echoes can be used to substantially increase slice coverage. In multislice mode, the resulting incidental magnetization transfer decreased brain signal but had minimal effect on measured T2 values.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".