<title>Numerical environment for simulating 3D angiographic imaging of the coronary arteries</title>
Bibliographic record
Abstract
A realistic numerical environment for simulating three-dimensional (3D) angiographic imaging of the coronary arteries has been developed. Through numerical simulation we propose to optimize acquisition and gating strategies, aiding in the design of 3D coronary imaging systems. We have previously developed a dynamic model of the coronary arteries, based on a high-resolution 3D image of an excised human heart, which was perfused with iodinated contrast agent. To mimic the motion of the arteries during the cardiac cycle, the motion of the vessel branch points was determined from cine bi-plane coronary angiograms of a patient with vessel anatomy similar to the excised heart. The static image was then non-linearly deformed to produce a sequence of volumetric images, with isotropic 0.4-mm resolution, representing the motion of the coronary arteries throughout the cardiac cycle. To simulate different acquisition strategies we have developed an algorithm to forward project through the volume data sets. The geometry of the CT system used to acquire the original 3D image of the static heart is mimicked in the re-projection algorithm. Thus, prospective radiographic projections corresponding to any projection-angle can be produced for any time -point throughout the cardiac cycle. Combining re-projections from selected time-points and view angles enables the evaluation of various acquisition and gating strategies.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.009 |
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".