Relaxivity‐iron calibration in hepatic iron overload: Predictions of a Monte Carlo model
Bibliographic record
Abstract
PURPOSE: R2* (1/T2*) and single echo R2 (1/T2) have been calibrated to liver iron concentration (LIC) in patients with thalassemia and transfusion-dependent sickle cell disease at 1.5T. The R2*-LIC relationship is linear, whereas that of R2 is curvilinear. However, the increasing popularity of high-field scanners requires generalizing these relationships to higher field strengths. In this study, we tested the hypothesis that numerical simulation can accurately determine the field dependence of iron-mediated transverse relaxation rates. METHODS: We previously replicated the calibration curves between R2 and R2* and iron at 1.5T using Monte Carlo models incorporating realistic liver structure, iron deposit susceptibility, and proton mobility. In this paper, we extend our model to predict relaxivity-iron calibrations at higher field strengths. Predictions were validated by measuring R2 and R2* at 1.5T and 3T in six β-thalassemia major patients. RESULTS: Predicted R2* increased twofold at 3T from 1.5T, whereas R2 increased by a factor of 1.47. Patient data exhibited a coefficient of variation of 3.6% and 7.2%, respectively, to the best-fit simulated data. Simulations over the range 0.25T-7T showed R2* increasing linearly with field strength, whereas R2 exhibited a concave-downward relationship. CONCLUSION: A model-based approach predicts alterations in relaxivity-iron calibrations with field strength without repeating imaging studies. The model may generalize to alternative pulse sequences and tissue iron distribution.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".