Angle‐independent measure of motion for image‐based gating in 3D coronary angiography
Bibliographic record
Abstract
The role of three-dimensional (3D) image guidance for interventional procedures and minimally invasive surgeries is increasing for the treatment of vascular disease. Currently, most interventional procedures are guided by two-dimensional x-ray angiography, but computed rotational angiography has the potential to provide 3D geometric information about the coronary arteries. The creation of 3D angiographic images of the coronary arteries requires synchronization of data acquisition with respect to the cardiac cycle, in order to minimize motion artifacts. This can be achieved by inferring the extent of motion from a patient's electrocardiogram (ECG) signal. However, a direct measurement of motion (from the 2D angiograms) has the potential to improve the 3D angiographic images by ensuring that only projections acquired during periods of minimal motion are included in the reconstruction. This paper presents an image-based metric for measuring the extent of motion in 2D x-ray angiographic images. Adaptive histogram equalization was applied to projection images to increase the sharpness of coronary arteries and the superior-inferior component of the weighted centroid (SIC) was measured. The SIC constitutes an image-based metric that can be used to track vessel motion, independent of apparent motion induced by the rotational acquisition. To evaluate the technique, six consecutive patients scheduled for routine coronary angiography procedures were studied. We compared the end of the SIC rest period (rho) to R-waves (R) detected in the patient's ECG and found a mean difference of 14 +/- 80 ms. Two simultaneous angular positions were acquired and rho was detected for each position. There was no statistically significant difference (P = 0.79) between rho in the two simultaneously acquired angular positions. Thus we have shown the SIC to be independent of view angle, which is critical for rotational angiography. A preliminary image-based gating strategy that employed the SIC was compared to an ECG-based gating strategy in a porcine model. The image-based gating strategy selected 61 projection images, compared to 45 selected by the ECG-gating strategy. Qualitative comparison revealed that although both the SIC-based and ECG-gated reconstructions decreased motion artifact compared to reconstruction with no gating, the SIC-based gating technique increased the conspicuity of smaller vessels when compared to ECG gating in maximum intensity projections of the reconstructions and increased the sharpness of a vessel cross section in multi-planar reformats of the reconstruction.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".