Free‐breathing variable flip angle balanced SSFP cardiac cine imaging with reduced SAR at 3T
Bibliographic record
Abstract
PURPOSE: To develop a free-breathing variable flip angle (VFA) balanced steady-state free precession (bSSFP) cardiac cine imaging technique with reduced specific absorption rate (SAR) at 3 Tesla. METHODS: Free-breathing VFA (FB-VFA) images in the short-axis and four-chamber views were acquired using an optimal VFA scheme, then compared with conventional breath-hold constant flip angle (BH-CFA) acquisitions. Two cardiac MRI experts used a 5-point scale to score images from healthy subjects (N = 10). The left ventricular ejection fraction, end diastolic volume (LVEDV), end systolic volume, stroke volume (LVSV), and end diastolic myocardial mass (LVEDM) were determined by manual contour analysis for BH-CFA and FB-VFA. A pilot evaluation of FB-VFA was performed in one patient with Duchenne muscular dystrophy. RESULTS: FB-VFA SAR was 25% lower than BH-CFA with similar blood-myocardium contrast. The qualitative FB-VFA score was lower than the BH-CFA for the short-axis (3.1 ± 0.5 versus 4.3 ± 0.8; P < 0.05) and the four-chamber view (3.4 ± 0.4 versus 4.6 ± 0.6; P < 0.05). The LVEDV and the LVSV were 5% and 12% larger (P < 0.05) for FB-VFA compared with BH-CFA. There was no difference in LVEDM. CONCLUSION: FB-VFA bSSFP cardiac cine imaging decreased the SAR at 3T with image quality sufficient to perform cardiac functional analysis. Magn Reson Med 76:1210-1216, 2016. © 2015 Wiley Periodicals, Inc.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".