Analytical characterization of RF phase‐cycled balanced steady‐state free precession
Bibliographic record
Abstract
Abstract Balanced steady‐state free precession (bSSFP) techniques, commonly called True‐FISP, FIESTA, or balanced FFE, are rapid, efficient, high signal‐to‐noise ratio sequences used in angiographic, cardiac and abdominal MR imaging. One of their major drawbacks is their sensitivity to off‐resonance effects arising from Bo inhomogeneities, which can produce the familiar dark bands in the images. Radio frequency (RF) phase cycling is known to minimize this artifact. Although RF phase‐cycled bSSFP is an accepted and firmly established technique, a simple and intuitive analytical signal equation has not yet been provided. Here, a complete theoretical framework is developed from which insightful characterizations such as signal uniformity, optimal flip angle, SNR efficiency, and banding effect minimization are provided. The analytical predictions and guidelines are substantiated via direct numerical simulations for clinically relevant tissues (e.g., water‐like organs and lipids) using various acquisition conditions, and also verified experimentally with phantoms. We demonstrate that the banding artifact can be virtually eliminated, even for phantoms immersed in significantly nonuniform magnetic fields. © 2009 Wiley Periodicals, Inc. Concepts Magn Reson Part A 34A: 133–143, 2009.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".