3D Motion Compensated Tomographic Reconstruction of Coronary Stents from X-ray Rotational Sequence : An Experimental Study
Bibliographic record
Abstract
Percutaneous transluminal coronary angioplasty consists in conducting a balloon and a stent through a coronary lesion and deploying the stent by balloon inflation. A coronary stent is a 3D complex mesh hardly visible in X-Ray images. The control of stent deployment is difficult from the 2D projection images inspection although insufficient deployment of the stent may lead to post intervention complications. In previous works, we have proposed a way to improve the clinical control of the intervention in the continuity of the angiographic procedure. We suggest to reconstruct 3D stent images from a set of 2D cone-beam projections acquired in rotational acquisition mode, using a motion compensation technique. In this paper, we investigate the feasibility of this method on coronary stents in-vivo. We first introduce one synthetic realistic case to illustrate the incorrect deployment problematic and control requirements. In-vivo experiments have been carried out on ten pigs, the porcine model is known to get anatomical and physiological similarities with humans. The cross-sectional area and the diameter along the motion compensated reconstructed deployed stent are measured in 3D slices (parallel or orthogonal to the axis of the stent). This feasibility study shows that a three-dimensional stent tomographic reconstruction can be obtained in a conventional interventional suite from a rotational X-ray sequence. So 3D quantitative measurements not available from the actual angiographic procedure are provided to the physician and help him to assess the quality of his intervention.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".