Transverse relaxation and flip angle mapping: Evaluation of simultaneous and independent methods using multiple spin echoes
Bibliographic record
Abstract
PURPOSE: To evaluate transverse relaxation (T2) and flip angle maps derived from signal pathway modeling of multiple spin echoes using simultaneous or independent T2 and flip angle fitting. METHODS: We examined different approaches to indirect and stimulated echo compensated T2 relaxometry from multiple spin echoes to evaluate both T2 and flip angle accuracy in simulation, phantom, and human brain. Signal pathways were modeled with or without independent flip angle maps using either Bloch simulations, or Extended Phase Graph (EPG) with Fourier or Shinnar-Le Roux approximation of slice profiles. RESULTS: Slice-selective decay curves differ substantially between models. Inaccurate flip angles are obtained with EPG methods, although T2 values are relatively accurate. Providing measured flip angles to EPG methods yields erroneous T2. Bloch methods improve both T2 and flip angle results. Simultaneous fitting can suffer from flip angle redundancy yielding multiple T2 solutions, particularly in low signal-to-noise ratio cases. CONCLUSION: EPG fitting provides reasonably accurate T2, but is limited by poor accuracy in resulting flip angles, and T2 errors increase when flip angles are provided. Bloch simultaneous fitting of T2 and flip angle provides excellent results, but can be limited by multiple solutions which can be overcome by including a flip angle map. Magn Reson Med 77:2057-2065, 2017. © 2016 International Society for MagneticResonance in Medicine.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".