Combined T<sub>2</sub>‐preparation and multidimensional outer volume suppression for coronary artery imaging with 3D cones trajectories
Bibliographic record
Abstract
Purpose To develop a modular magnetization preparation sequence for combined T2‐preparation and multidimensional outer volume suppression (OVS) for coronary artery imaging. Methods A combined T2‐prepared 1D OVS sequence with fat saturation was defined to contain a 90°−60180°60 composite nonselective tip‐down pulse, two 180°Y hard pulses for refocusing, and a −90° spectral‐spatial sinc tip‐up pulse. For 2D OVS, 2 modules were concatenated, selective in X and then Y. Bloch simulations predicted robustness of the sequence to B0 and B1 inhomogeneities. The proposed sequence was compared with a T2‐prepared 2D OVS sequence proposed by Luo et al, which uses a spatially selective 2D spiral tip‐up. The 2 sequences were compared in phantom studies and in vivo coronary artery imaging studies with a 3D cones trajectory. Results Phantom results demonstrated superior OVS for the proposed sequence compared with the Luo sequence. In studies on 15 healthy volunteers, the proposed sequence had superior image edge profile acutance values compared with the Luo sequence for the right (P < .05) and left (P < .05) coronary arteries, suggesting superior vessel sharpness. The proposed sequence also had superior signal‐to‐noise ratio (P < .05) and passband‐to‐stopband ratio (P < .05). Reader scores and reader preference indicated superior coronary image quality of the proposed sequence for both the right (P < .05) and left (P < .05) coronary arteries. Conclusion The proposed sequence with concatenated 1D spatially selective tip‐ups and integrated fat saturation has superior image quality and suppression compared with the Luo sequence with 2D spatially selective tip‐up.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 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".