Effects of <scp>B<sub>1</sub></scp><sup>+</sup> Heterogeneity on Spin Echo‐Based Liver Iron Estimates
Bibliographic record
Abstract
Background Liver iron concentration (LIC) measured by MRI has become the clinical reference standard for managing iron overload in chronically transfused patients. Transverse relaxivity (R2 or R2*) measurements are converted to LIC units using empirically derived calibration curves. Hypothesis That flip angle (FA) error due to B1+ spatial heterogeneity causes significant LIC quantitation error. B1+ scale (b1, [FAactual/FAspecified]) variation is a major problem at 3 T which could reduce the accuracy of transverse relaxivity measurements. Study Type Prospective. Population Forty‐seven subjects with chronic transfusional iron overload undergoing clinically indicated LIC assessment. Field Strength/Sequence 5 T/3 T dual‐repetition time B1+ mapping sequence Assessment We quantified the average/standard deviation b1 in the right and left lobes of the liver from B1+ maps acquired at 1.5 T and 3 T. The impact of b1 variation on spin echo LIC estimates was determined using a Monte Carlo model. Statistical Tests Mean, median, and standard deviation in whole liver and right and left lobes; two‐sided t‐test between whole‐liver b1 means. Results Average b1 within the liver was 99.3% ± 12.3% at 1.5 T versus 69.6% ± 14.6% at 3 T and was independent of iron burden (P < 0.05). Monte Carlo simulations demonstrated that b1 systematically increased R2 estimates at lower LIC (<~25 mg/g at 1.5 T, <~15 mg/g at 3 T) but flattened or even inverted the R2‐LIC relationship at higher LIC (≥~25 mg/g to 1.5 T, ≥~15 mg/g to 3 T); changes in the R2‐LIC relationship were symmetric with respect to over and under excitation and were similar at 1.5 T and 3 T (for the same R2 value). The R2*‐LIC relationship was independent of b1. Conclusion Spin echo R2 measurement of LIC at 3 T is error‐prone without correction for b1 errors. The impact of b1 error on current 1.5 T spin echo‐based techniques for LIC quantification is large enough to introduce measurable intersubject variability but the in vivo effect size needs a dedicated validation study. Level of Evidence 1. Technical Efficacy Stage 2.
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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 | 0.006 | 0.018 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".