‐corrected water–fat imaging using compressed sensing and parallel imaging
Bibliographic record
Abstract
PURPOSE: To demonstrate an approach to water-fat separation with R2* correction using compressed sensing and parallel imaging. METHODS: Acquisition times for chemical shift based water-fat separation imaging are lengthy, and many applications rely on image acceleration techniques. In this study, we present an integrated compressed sensing, parallel imaging, R2* corrected water-fat separation technique for water-fat imaging of highly accelerated acquisitions. Reconstruction times are reduced using coil compression. RESULTS: The proposed technique is demonstrated using a customized IDEAL-SPGR pulse sequence to acquire retrospectively and prospectively undersampled datasets of the liver, calf, knee, and abdominal cavity. This technique is shown to offer comparable image quality relative to fully sampled reference images for a range of acceleration factors. At high acceleration factors, this technique is shown to offer improved image quality over parallel imaging. CONCLUSION: A technique is described that uses compressed sensing and parallel imaging to reconstruct R2*-corrected water and fat images from accelerated datasets. Acceleration factors as high as 7.0 are shown with excellent image quality. These high acceleration factors enable water-fat separation with higher resolution or greater anatomical coverage in breath-hold applications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".