Characterizing coronary motion and its effect on MR coronary angiography—Initial experience
Bibliographic record
Abstract
PURPOSE: To characterize coronary artery motion as a prescan procedure to select the optimum scan setting that will produce high-resolution images. MATERIALS AND METHODS: A 2D real-time scan was used to image the major coronary arteries during breath-holding and free-breathing conditions. With the use of the 2D images, motion displacement of each artery was measured along three axes. Motion data obtained from a computer simulation were used to estimate point-spread functions (PSFs) associated with different high-resolution spiral acquisition strategies, including real-time, cardiac-gated, and respiratory-gated acquisitions. The simulation output determined the optimum acquisition and scan parameters that would produce the highest-spatial-resolution images of the coronary arteries. The effects of heart rate (HR), extended breath-holding, and number of slices per heart cycle were also investigated. RESULTS: Substantial variations in coronary motion occur among individuals, which directly influences the optimum parameters for a high-resolution scan. Lower HRs and longer breath-holds yield substantially increased spatial resolution. The maximum number of slices per heart cycle can also be determined to minimize slice-to-slice distortion. CONCLUSION: The results suggest that to obtain high-resolution coronary images, one should perform a prescan coronary-motion characterization for each individual so that the scan parameters can be optimized before data acquisition.
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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 | 0.003 | 0.007 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".