Rapid pacing rotational angiography with three-dimensional reconstruction: use and benefits in structural heart disease interventions.
Bibliographic record
Abstract
AIMS: High quality three-dimensional imaging is one of the cornerstones in structural heart disease interventions. Current mainstream technology to acquire three-dimensional imaging utilises computed tomography or magnetic resonance imaging. Incorporation of these data with conventional angiographic images may not be sufficient. We describe a new imaging technique consisting of rotational angiography combined with rapid pacing to obtain real-time, high-quality, three-dimensional images in the catheterisation laboratory. METHODS AND RESULTS: Rotational angiography is performed with breath holding and rapid pacing on a large format digital flat-panel angiographic system. During a 200 degrees rotation, 150 angiographic images are acquired in five seconds and automatically reconstructed in less than 30 seconds. This imaging technique was used in six patients (mean age 32 +/- 10 years) to guide structural heart disease interventions. No complications were associated with rapid pacing. This imaging technique allowed acquisition of high-quality, three-dimensional images with a low volume of contrast media. Volume renderings helped appreciation of the lesions and optimisation of the working views. Multiplanar visualisation allowed true orthogonal measurements of vascular diameter during the procedures. CONCLUSIONS: The advantages of this imaging technique include rapid image acquisition and precise imaging of complex structures using low volume of contrast media.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".