US–Fluoroscopy Registration for Transcatheter Aortic Valve Implantation
Bibliographic record
Abstract
Transcatheter aortic valve implantation is a minimally invasive alternative to open-heart surgery for aortic stenosis in which a stent-based bioprosthetic valve is delivered into the heart on a catheter. Limited visualization during this procedure can lead to severe complications. Improved visualization can be provided by live registration of transesophageal echo (TEE) and fluoroscopy images intraoperatively. Since the TEE probe is always visible in the fluoroscopy image, it is possible to track it using fiducial-based single-perspective pose estimation. In this study, inherent probe tracking performance was assessed, and TEE to fluoroscopy registration accuracy and robustness were evaluated. Results demonstrated probe tracking errors of below 0.6 mm and 0.2°, a 2-D RMS registration error of 1.5 mm, and a tracking failure rate of below 1%. In addition to providing live registration and better accuracy and robustness compared to existing TEE probe tracking methods, this system is designed to be suitable for clinical use. It is fully automatic, requires no additional operating room hardware, does not require intraoperative calibration, maintains existing procedure and imaging workflow without modification, and can be implemented in all cardiac centers at extremely low cost.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 0.001 |
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".