Monoplane 3D reconstruction of mapping ablation catheters: a feasibility study
Bibliographic record
Abstract
PURPOSE: Radiofrequency (RF) catheter ablation has transformed treatment for arrhythmias and has become first-line therapy for some tachycardias. The precise localization of the arrhythmogenic site and the positioning of the RF catheter over that site are problematic: they can impair the efficiency of the procedure and are time consuming (several hours). This study evaluates the feasibility of using only single plane C-arm images in order to estimate the 3D coordinates of RF catheter electrodes in a cardiac phase. MATERIALS AND METHODS: The method makes use of a priori 3D model of the RF mapping catheter assuming rigid body motion equations in order to estimate the 3D locations of the catheter tip-electrodes in single view C-arm fluoroscopy images. Validation is performed on both synthetic and clinical data using computer simulation models. The authors' monoplane reconstruction algorithm is applied to a 3D helix mimicking the shape of a catheter and undergoing solely rigid motion. Similarly, the authors test the feasibility of recovering nonrigid motion by applying their method on true 3D coordinates of 13 ventricular markers from a sheep's ventricle. RESULTS: The results of this study showed that the proposed monoplane algorithm recovers rigid motion adequately when using the spatial positions of a catheter in six consecutive C-arm image frames yielding maximum 3D root mean squares errors of 4.3 mm. On the other hand, the suggested algorithm did not recover nonrigid motion precisely as suggested by a maximum 3D root mean square value of 8 mm. CONCLUSION: Since RF catheter electrodes are rigid structures, the authors conclude that there is promise in recovering the 3D coordinates of the electrodes when making use of only single view images. Future work will involve adding nonrigid motion equations to their algorithm, which will then be applied to actual clinical data.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".