Quantification of Hepatic Steatosis with T1-independent, T2*-corrected MR Imaging with Spectral Modeling of Fat: Blinded Comparison with MR Spectroscopy
Bibliographic record
Abstract
PURPOSE: To prospectively compare an investigational version of a complex-based chemical shift-based fat fraction magnetic resonance (MR) imaging method with MR spectroscopy for the quantification of hepatic steatosis. MATERIALS AND METHODS: This study was approved by the institutional review board and was HIPAA compliant. Written informed consent was obtained before all studies. Fifty-five patients (31 women, 24 men; age range, 24-71 years) were prospectively imaged at 1.5 T with quantitative MR imaging and single-voxel MR spectroscopy, each within a single breath hold. The effects of T2 correction, spectral modeling of fat, and magnitude fitting for eddy current correction on fat quantification with MR imaging were investigated by reconstructing fat fraction images from the same source data with different combinations of error correction. Single-voxel T2-corrected MR spectroscopy was used to measure fat fraction and served as the reference standard. All MR spectroscopy data were postprocessed at a separate institution by an MR physicist who was blinded to MR imaging results. Fat fractions measured with MR imaging and MR spectroscopy were compared statistically to determine the correlation (r(2)), and the slope and intercept as measures of agreement between MR imaging and MR spectroscopy fat fraction measurements, to determine whether MR imaging can help quantify fat, and examine the importance of T2 correction, spectral modeling of fat, and eddy current correction. Two-sided t tests (significance level, P = .05) were used to determine whether estimated slopes and intercepts were significantly different from 1.0 and 0.0, respectively. Sensitivity and specificity for the classification of clinically significant steatosis were evaluated. RESULTS: Overall, there was excellent correlation between MR imaging and MR spectroscopy for all reconstruction combinations. However, agreement was only achieved when T2 correction, spectral modeling of fat, and magnitude fitting for eddy current correction were used (r(2) = 0.99; slope ± standard deviation = 1.00 ± 0.01, P = .77; intercept ± standard deviation = 0.2% ± 0.1, P = .19). CONCLUSION: T1-independent chemical shift-based water-fat separation MR imaging methods can accurately quantify fat over the entire liver, by using MR spectroscopy as the reference standard, when T2 correction, spectral modeling of fat, and eddy current correction methods are used.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".