Factors affecting the correlation coefficient template matching algorithm with application to real-time 2-D coronary artery MR imaging
Bibliographic record
Abstract
This paper characterizes factors affecting the accuracy of the correlation coefficient (CC) template matching algorithm, as applied to motion tracking from two-dimensional real-time coronary artery magnetic resonance images. The performance of this algorithm is analyzed in the presence of both random and systematic error. In the presence of random error, it is shown that a necessary and sufficient condition for accurate motion tracking is a large CC difference-to-noise ratio (CCDNR). The CCDNR itself is in turn affected by five factors: image and template size, image and template structure, and the magnitude of the noise. Techniques are introduced for manipulating some of these factors in order to increase the CCDNR for greater motion tracking accuracy. In the presence of superimposed systematic error it is shown that, while large CCDNR is necessary, it alone is not sufficient to ensure accurate motion tracking. Techniques are developed for improving motion tracking accuracy that minimize the effects of systematic error, while maintaining an adequate CCDNR level. The ability of these techniques to improve motion tracking accuracy is demonstrated both in phantoms and in coronary artery images.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".