Free Breathing Real-Time Cardiac Cine Imaging With Improved Spatial Resolution at 3 T
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to evaluate free-breathing single-shot real-time cine imaging for functional cardiac imaging at 3 T with increased spatial resolution. Special emphasis of this study was placed on the influence of parallel imaging techniques. MATERIALS AND METHODS: Gradient echo phantom images were acquired with GRAPPA and modified SENSE reconstruction using both integrated and separate reference scans as well as TGRAPPA and TSENSE. In vivo measurements were performed for GRAPPA reconstruction with an integrated and a separate reference scan, as well as TGRAPPA using balanced steady-state free precession protocols. Three clinical protocols, rtLRInt (Tres = 51.3 milliseconds; voxel, 2.5 × 5.0 × 10 mm³), rtMRSep (Tres = 48.8 milliseconds; voxel, 1.9 × 3.1 × 10 mm³), and rtHRSep (Tres = 48.3 milliseconds; voxel, 1.6 × 2.6 × 10 mm), were investigated on 20 volunteers using GRAPPA reconstruction with internal as well as separate reference scans. End-diastolic volume, end-systolic volume, ejection fraction, peak ejection rate, peak filling rate, and myocardial mass were evaluated for the left ventricle and compared with an electrocardiogram-triggered segmented readout cine protocol used as standard of reference. All studies were performed at 3 T. RESULTS: Phantom and in vivo data demonstrate that the combination of GRAPPA reconstruction with a separate reference scan provides an optimal compromise of image quality as well as spatial and temporal resolution. Functional values (P values) for the standard of reference, rtLRInt, rtMRSep, and rtHRSep end-diastolic volume were 141 ± 24 mL, 138 ± 21 mL, 138 ± 19 mL, and 128 ± 33 mL, respectively (P = 0.7, 0.7, 0.4); end-systolic volume, 55 ± 15 mL, 61 ± 14 mL, 58 ± 12 mL, and 55 ± 20 mL, respectively (P = 0.23, 0.43, 0.62); ejection fraction, 61% ± 5%, 57% ± 5%, 58% ± 4%, and 56% ± 8%, respectively (P = 0.01, 0.11, 0.06); peak ejection rate, 481 ± 73 mL/s, 425 ± 62 mL/s, 434 ± 67 mL/s, and 381 ± 86 mL/s, respectively (P = 0.03, 0.04, 0.01); peak filling rate, 555 ± 80 mL/s, 480 ± 70 mL/s, 500 ± 70 mL/s, and 438 ± 108 mL/s, respectively (P= 0.007, 0.05, 0.004); and myocardial mass, 137 ± 26 g, 141 ± 25 g, 141 ± 23 g, and 130 ± 31 g, respectively (P = 0.62, 0.54, 0.99). CONCLUSIONS: Using a separate reference scan and high acceleration factors up to R = 6, single-shot real-time cardiac imaging offers adequate temporal and spatial resolution for accurate assessment of global left ventricular function in free breathing with short examination times.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".