Limitations of skipping echoes for exponential T<sub>2</sub> fitting
Bibliographic record
Abstract
Background Exponential fitting of multiecho spin echo sequences with skipped echoes is still commonly used for quantification of transverse relaxation (T 2 ). Purpose To examine the efficacy of skipped echo methods for T 2 quantification against computational modeling of the exact signal decay. Study Type Prospective comparison of methods. Subjects/Phantom Eight volunteers were imaged at 4.7T, six volunteers at 1.5T, and phantoms ([MnCl 2 ] = 68–270 mM). Field Strength/Sequence 1.5T and 4.7T; multiple‐echo spin echo. Assessment Exponential fitting for T 2 using all echoes, skipping the first echo or skipping all odd echoes, compared with Bloch simulations. Resulting T 2 values were examined over a range of T 2 (10–150 msec), refocusing flip angles (90–270°), and echo train lengths (ETL = 6–32). Statistical Tests Shapiro–Wilk tests and Q‐Q plots were used to check for normality of data. Paired sample t ‐tests and Wilcoxon rank tests were used to compare fitting models using α = 0.05. Multiple comparisons were accounted for with Bonferroni correction. Results In examined regions of interest, typical incorrect estimation of T 2 ranged from 23–39% for exponential fitting of all echoes, or 15–32% for skipped echo methods. In vivo, T 2 estimation error was reduced to as little as 10% with skipped echo methods using 180° refocusing and ETL = 8, although error varied due to refocusing angle, T 2 , and ETL. In vivo, skipped echo T 2 values were significantly different than all echo exponential fitting ( P < 0.004), but also were significantly different from reference values ( P < 0.002, except frontal white matter). Simulations showed skipping the first echo was the most effective form of exponential fitting, in particular for T 2 <50 msec and ETL = 8, with potential to reduce T 2 errors to 10%, depending on refocusing angle and T 2 . Data Conclusion Skipping echoes is insufficient for avoiding stimulated echo contamination. Resulting T 2 errors depend on a complicated interplay of T 2 , refocusing angle, and ETL. Modeling of the multiecho sequence is recommended. Level of Evidence : 2 Technical Efficacy : Stage 1 J. Magn. Reson. Imaging 2018;47:1432–1440.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".