Adaptive Averaging for Improved SNR in Real-Time Coronary Artery MRI
Bibliographic record
Abstract
A technique has been developed for combining a series of low signal-to-noise ratio (SNR) real-time magnetic resonance (MR) images to produce composite images with high SNR and minimal artifact in the presence of motion. The main challenge is identifying a set of real-time images with sufficiently small systematic differences to avoid introducing significant artifact into the composite image. To accomplish this task, one must: 1) identify images identical within the limits of noise; 2) detect systematic errors within such images with sufficient sensitivity. These steps are achieved by evaluating the correlation coefficient (CC) between regions in prospective images and a template containing the anatomy of interest. Images identical within noise are selected by comparing the measured CC values to the theoretical distribution expected due to noise. Sensitivity for systematic error depends on the SNR of the CC (=SNR(CCmax)), which in turn depends on the noise, and the template size and structure. By varying the template size, SNR(CCmax) may be altered. Experiments on phantoms and coronary artery images demonstrate that the SNR(CCmax) necessary to avoid introducing significant artifact varies with the target composite SNR. The future potential of this technique is demonstrated on high-resolution (approximately 0.9 mm), reduced field-of-view real-time coronary images.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".