Three-Dimensional Trajectory Assessment of an IVUS Transducer From Single-Plane Cineangiograms: A Phantom Study
Bibliographic record
Abstract
The recovery of the three-dimensional (3-D) path of the transducer used during an intravascular ultrasound (IVUS) examination is of primary importance to assess the exact 3-D shape of the vessel under study. Traditionally, the reconstruction is done by simply stacking the images during the pullback, or more recently using biplane angiography to recover the vessel curvature. In this paper, we explain, how single-plane angiography can be used with two projection models, to perform this task. Two types of projection geometry are analyzed: weak-perspective and full-perspective. In weak-perspective projection geometry, the catheter path can be reconstructed without prior transducer depth information. With full-perspective projection geometry, precise depth location of reference points are needed in order to minimize the error of the recovered transducer angle of incidence. The transducer angulation reconstruction is based on the foreshortening effect as seen from the X-ray images. By comparing the measured to the true transducer length, we are able to get its incidence angle. The transducer trajectory is reconstructed by stitching together the different estimated angulations obtained from each image in a cineangiogram sequence. The method is described and validated on two helical vessel phantoms, giving on average a reconstructed path that is less than 2 mm distant from the true path when using full-perspective projection.
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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 | 0.004 |
| 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 | 0.000 |
| 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".