Free‐breathing cardiac MR with a fixed navigator efficiency using adaptive gating window size
Bibliographic record
Abstract
A respiratory navigator with a fixed acceptance gating window is commonly used to reduce respiratory motion artifacts in cardiac MR. This approach prolongs the scan time and occasionally yields an incomplete dataset due to respiratory drifts. To address this issue, we propose an adaptive gating window approach in which the size and position of the gating window are changed adaptively during the acquisition based on the individual's breathing pattern. The adaptive gating window tracks the breathing pattern of the subject throughout the scan and adapts the size and position of the gating window such that the gating efficiency is always fixed at a constant value. To investigate the image quality and acquisition time, free breathing cardiac MRI, including both targeted coronary MRI and late gadolinium enhancement imaging, was performed in 67 subjects using the proposed navigator technique. Targeted coronary MRI was acquired from eleven healthy adult subjects using both the conventional and proposed adaptive gating window techniques. Fifty-six patients referred for cardiac MRI were also imaged using late gadolinium enhancement with the proposed adaptive gating window technique. Subjective and objective image assessments were used to evaluate the proposed method. The results demonstrate that the proposed technique allows free-breathing cardiac MRI in a relatively fixed time without compromising imaging quality due to respiratory motion artifacts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".