Manual and semi-automatic registration vs retrospective ECG gating for correction of cardiac motion
Bibliographic record
Abstract
A manual and a semi-automatic image registration method were compared with retrospective ECG (rECG) gating to correct for cardiac motion in myocardial perfusion (MBF) measurement. 5 beagles were used in 11 experiments. For each experiment a 30 s cine CT scan of the heart was acquired after contrast injection. For the manual method, a reference end-diastole (ED) image was selected from the first cardiac cycle. ED images in subsequent cardiac cycles were manually selected to match the shape of the reference ED image. For each cardiac cycle in the semi-automatic method, the image with the maximum area and the most similar shape to the selected image of the previous cardiac cycle was chosen as ED image. MBFs were calculated from the images registered by the three methods and compared. The averages of the difference of MBFmanual and MBFsemi-auto and MBFrECG in the lateral free wall of LV were 3.6 and 3.4 ml/min/100g respectively. The corresponding standard deviations from the mean were 9.1 and 28.3 ml/min/100g respectively. We concluded from these preliminary results that image registration methods were better than rECG gating for correcting heart, which should facilitate more precise measurement of MBF.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".