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 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 | 0.001 |
| 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) | 0.001 | 0.001 |
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".