Subject‐specific estimation of respiratory navigator tracking factor for free‐breathing cardiovascular MR
Bibliographic record
Abstract
A mean respiratory navigator tracking factor of 0.6 is commonly used to estimate the respiratory motion of the heart from the displacement of the right hemi-diaphragm. A constant tracking factor can generate significant residual error in estimation of the respiratory motion of the heart for the cases where the actual tracking factor highly deviates from 0.6. In this study, we implemented and evaluated a robust method to calculate a subject-specific tracking factor for free-breathing high resolution cardiac MR. The subject-specific tracking factor was calculated from two consecutive navigator signals placed on the right hemi-diaphragm and the basal left ventricle in a training phase. To verify the accuracy of the estimated subject-specific tracking factor, nineteen subjects were recruited for comparing the estimated tracking factor in real-time with an image-based tracking factor, calculated off-line. Subsequently, in seven adult subjects, whole-heart or targeted coronary artery MR images were acquired using the estimated subject-specific tracking factor and visually compared with those acquired using a constant (0.6) tracking factor. It was shown that the proposed method can accurately estimate the subject-specific tracking factor and improve the quality of coronary images when the subject-specific tracking factor differs from 0.6.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".