Magnetization evolution in balanced steady-state free precession with continuously moving table
Bibliographic record
Abstract
Diagnostic imaging of systemic disorders, such as peripheral vascular diseases, requires a field-of-view (FOV) larger than the local FOV available on clinical MR scanners. The continuously moving table (CMT) method acquires large FOV images in a single acquisition. Balanced steady-state free precession (bSSFP) is an attractive candidate for the CMT method due to its short repetition time and high signal-to-noise ratio. However, introducing table motion during data acquisition perturbs the magnetization evolution towards steady state. In this paper, a computer model was developed to simulate the bSSFP magnetization evolution in the presence of table motion. From these simulations, predictions were made about the maximum table velocities that would allow the magnetizations of specific tissues to evolve to the theoretical steady-state values. These predicted maximum table velocities were then successfully verified in vivo with bSSFP CMT acquisitions. For an imaging FOV
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".