Applications of stimulated echo correction to multicomponent <i>T</i><sub>2</sub> analysis
Bibliographic record
Abstract
We propose a multicomponent fitting algorithm for multiecho T(2) data which allows for correction of T(2) distributions in the presence of stimulated echoes. Tracking the population of spins in many coherence pathways via the iterated method of the Extended Phase Graph algorithm allows for accurate quantification of echo magnitudes. The resulting decay curves allow for correction of errors due to nonideal refocusing pulses as a result of inhomogeneities in the B(1) transmit field. Non-Negative Least Squares fitting is used to quantify the magnitude of T(2) components at various T(2) values. This method, allowing calculation of the T(2) distribution with simultaneous extraction of the refocusing pulse flip angle, requires no change to image acquisition procedures and no extra data input. Validation by means of both simulations and in vivo data shows excellent interscan reproducibility while vastly improving the accuracy of extracted T(2) parameters in voxels where poor B(1) homogeneity leads to refocusing pulse flip angles significantly less than 180°. Most notably, myelin water fraction values in these regions are found to have increased consistency and accuracy.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".