Quantification of hepatic steatosis with MRI: The effects of accurate fat spectral modeling
Bibliographic record
Abstract
PURPOSE: To develop a chemical-shift-based imaging method for fat quantification that accounts for the complex spectrum of fat, and to compare this method with MR spectroscopy (MRS). Quantitative noninvasive biomarkers of hepatic steatosis are urgently needed for the diagnosis and management of nonalcoholic fatty liver disease (NAFLD). MATERIALS AND METHODS: Hepatic steatosis was measured with "fat-fraction" images in 31 patients using a multiecho chemical-shift-based water-fat separation method at 1.5T. Fat-fraction images were reconstructed using a conventional signal model that considers fat as a single peak at -210 Hz relative to water ("single peak" reconstruction). Fat-fraction images were also reconstructed from the same source images using two methods that account for the complex spectrum of fat; precalibrated and self-calibrated "multipeak" reconstruction. Single-voxel MRS that was coregistered with imaging was performed for comparison. RESULTS: Imaging and MRS demonstrated excellent correlation with single peak reconstruction (r(2) = 0.91), precalibrated multipeak reconstruction (r(2) = 0.94), and self-calibrated multipeak reconstruction (r(2) = 0.91). However, precalibrated multipeak reconstruction demonstrated the best agreement with MRS, with a slope statistically equivalent to 1 (0.96 +/- 0.04; P = 0.4), compared to self-calibrated multipeak reconstruction (0.83 +/- 0.05, P = 0.001) and single-peak reconstruction (0.67 +/- 0.04, P < 0.001). CONCLUSION: Accurate spectral modeling is necessary for accurate quantification of hepatic steatosis with 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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".