Variable‐density adaptive imaging for high‐resolution coronary artery MRI
Bibliographic record
Abstract
Variable-density (VD) spiral k-space acquisitions are used to acquire high-resolution (0.78 mm), motion-compensated images of the coronary arteries. Unlike conventional methods, information for motion compensation is obtained directly from the coronary anatomy itself. Specifically, periods of minimal coronary distortion are identified by applying the correlation coefficient template matching algorithm to real-time images generated from the inner, high-density portions of the VD spirals. Combining the data associated with these images together, high-resolution, motion-compensated coronary images are generated. Because coronary motion is visualized directly, the need for cardiac-triggering, breath-holding, and navigator echoes is eliminated. The motion compensation capability of the technique is determined by the inner-spiral spatial and temporal resolution. Results indicate that the best performance is achieved using inner-spiral images with high spatial resolution (1.6-2.9 mm), even though temporal resolution (four to six independent frames per second) suffers as a result. Image quality within the template region in healthy volunteers was found to be comparable to that achieved with cardiac-triggered breath-hold scans, although extended acquisition times of around 5 min were needed to overcome reduced SNR efficiency.
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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 | 0.000 |
| 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 | 0.000 |
| 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 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".