Linearity, Bias, Intrascanner Repeatability, and Interscanner Reproducibility of Quantitative Multidynamic Multiecho Sequence for Rapid Simultaneous Relaxometry at 3 T
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to evaluate the linearity, bias, intrascanner repeatability, and interscanner reproducibility of quantitative values derived from a multidynamic multiecho (MDME) sequence for rapid simultaneous relaxometry. MATERIALS AND METHODS: The NIST/ISMRM (National Institute of Standards and Technology/International Society for Magnetic Resonance in Medicine) phantom, containing spheres with standardized T1 and T2 relaxation times and proton density (PD), and 10 healthy volunteers, were scanned 10 times on different days and 2 times during the same session, using the MDME sequence, on three 3 T scanners from different vendors. For healthy volunteers, brain volumetry and myelin estimation were performed based on the measured T1, T2, and PD. The measured phantom values were compared with reference values; volunteer values were compared with their averages across 3 scanners. RESULTS: The linearity of both phantom and volunteer measurements in T1, T2, and PD values was very strong (R = 0.973-1.000, 0.979-1.000, and 0.982-0.999, respectively) The highest intrascanner coefficients of variation (CVs) for T1, T2, and PD were 2.07%, 7.60%, and 12.86% for phantom data, and 1.33%, 0.89%, and 0.77% for volunteer data, respectively. The highest interscanner CVs of T1, T2, and PD were 10.86%, 15.27%, and 9.95% for phantom data, and 3.15%, 5.76%, and 3.21% for volunteer data, respectively. Variation of T1 and T2 tended to be larger at higher values outside the range of those typically observed in brain tissue. The highest intrascanner and interscanner CVs for brain tissue volumetry were 2.50% and 5.74%, respectively, for cerebrospinal fluid. CONCLUSIONS: Quantitative values derived from the MDME sequence are overall robust for brain relaxometry and volumetry on 3 T scanners from different vendors. Caution is warranted when applying MDME sequence on anatomies with relaxometry values outside the range of those typically observed in brain tissue.
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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 | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.001 | 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".