Comparison of biphasic and reordered fat suppression for dynamic breast MRI
Bibliographic record
Abstract
PURPOSE: To optimize a reordered k-space acquisition that applies intermittent fat saturation (FS) pulses to allow for a time-efficient reduction of fat signal in breast MR images, and compare it with an elliptic-centric biphasic FS method in terms of the degree of fat suppression and speed. MATERIALS AND METHODS: The behavior of the fat and water signals under the influence of the reordered sequence was characterized. This allowed us to optimize the flip angle and visualize the expected artifacts by deriving the point spread function (PSF) of the fat signal. We compared the two sequences by acquiring images with a varying number of FS pulses, with a corresponding difference in scan time. The quality of the images was assessed by comparison with images obtained with full fat suppression as measured by a root-mean-square (RMS) error metric. RESULTS: The reordered sequence allowed for an approximately twofold reduction in error compared to the biphasic sequence for the same scan time. With the reordered sequence and optimized scan parameters, we were able to reduce the time spent on fat suppression by as much as 99% with no discernible reduction in image quality. CONCLUSION: This method will allow robust fat suppression with virtually no extension in imaging time for dynamic contrast-enhanced (DCE)-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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".