T<sub>2</sub>‐weighted 3D fast spin echo imaging with water–fat separation in a single acquisition
Bibliographic record
Abstract
PURPOSE: To develop a robust 3D fast spin echo (FSE) T(2)-weighted imaging method with uniform water and fat separation in a single acquisition, amenable to high-quality multiplanar reformations. MATERIALS AND METHODS: The Iterative Decomposition of water and fat with Echo Asymmetry and Least squares estimation (IDEAL) method was integrated with modulated refocusing flip angle 3D-FSE. Echoes required for IDEAL processing were acquired by shifting the readout gradient with respect to the Carr-Purcell-Meiboom-Gill echo. To reduce the scan time, an alternative data acquisition using two gradient echoes per repetition was implemented. Using the latter approach, a total of four gradient echoes were acquired in two repetitions and used in the modified IDEAL reconstruction. RESULTS: 3D-FSE T(2)-weighted images with uniform water-fat separation were successfully acquired in various anatomies including breast, abdomen, knee, and ankle in clinically feasible scan times, ranging from 5:30-8:30 minutes. Using water-only and fat-only images, in-phase and out-of-phase images were reconstructed. CONCLUSION: 3D-FSE-IDEAL provides volumetric T(2)-weighted images with uniform water and fat separation in a single acquisition. High-resolution images with multiple contrasts can be reformatted to any orientation from a single acquisition. This could potentially replace 2D-FSE acquisitions with and without fat suppression and in multiple planes, thus improving overall imaging efficiency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 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 teacher head, 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".