Accurate liver T measurement of iron overload: A simulations investigation and in vivo study
Bibliographic record
Abstract
PURPOSE: To investigate the accuracy of T 2 liver iron quantification using different curve-fitting models under varying acquisition conditions, and to compare in iron-overloaded patients the reliability of rapid T 2 measurements against approved and slower T(2) protocols. MATERIALS AND METHODS: Simulations were conducted to assess the influence of various factors on the accuracy of T 2 measurement: curve-fitting model, signal-to-noise ratio (SNR), and echo time (TE) spacing. Fifty-four iron-overloaded pediatric patients were assessed using a standard T(2) and two variations of T 2 acquisitions. In both simulations and in vivo data, three analysis models were evaluated: monoexponential, constant offset, and truncated. RESULTS: Simulations show the truncated model provides the best accuracy but is susceptible to underestimating high iron species under low SNR or high minimum TE. In contrast, the offset model tends to overestimate but maintains the most reliable measurements across the relevant range of iron levels. Furthermore, a much lower SNR can be tolerated if the acquisition uses a low minimum TE. In vivo results confirm theoretical findings and show that T 2 measurements can be as reliable as those from approved and slower T(2) protocols. CONCLUSION: Guidelines are provided on choosing an appropriate model under specific noise conditions and acquisition schemes to ensure accurate and rapid T2 liver iron quantification.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 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".