T<sub>1</sub> independent, T<sub>2</sub>* corrected MRI with accurate spectral modeling for quantification of fat: Validation in a fat‐water‐SPIO phantom
Bibliographic record
Abstract
PURPOSE: To validate a T(1)-independent, T(2)*-corrected fat quantification technique that uses accurate spectral modeling of fat using a homogeneous fat-water-SPIO phantom over physiologically expected ranges of fat percentage and T(2)* decay in the presence of iron overload. MATERIALS AND METHODS: A homogeneous gel phantom consisting of vials with known fat-fractions and iron concentrations is described. Fat-fraction imaging was performed using a multiecho chemical shift-based fat-water separation method (IDEAL), and various reconstructions were performed to determine the impact of T(2)* correction and accurate spectral modeling. Conventional two-point Dixon (in-phase/out-of-phase) imaging and MR spectroscopy were performed for comparison with known fat-fractions. RESULTS: The best agreement with known fat-fractions over the full range of iron concentrations was found when T(2)* correction and accurate spectral modeling were used. Conventional two-point Dixon imaging grossly underestimated fat-fraction for all T(2)* values, but particularly at higher iron concentrations. CONCLUSION: This work demonstrates the necessity of T(2)* correction and accurate spectral modeling of fat to accurately quantify fat using MRI.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.000 |
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".