Intraoperative 3D stereo visualization for image-guided cardiac ablation
Bibliographic record
Abstract
There are commercial products which provide 3D rendered volumes, reconstructed from electro-anatomical mapping and/or pre-operative CT/MR images of a patient's heart with tools for highlighting target locations for cardiac ablation applications. However, it is not possible to update the three-dimensional (3D) volume intraoperatively to provide the interventional cardiologist with more up-to-date feedback at each instant of time. In this paper, we describe the system we have developed for real-time three-dimensional stereo visualization for cardiac ablation. A 4D ultrasound probe is used to acquire and update a 3D image volume. A magnetic tracking device is used to track the distal part of the ablation catheter in real time and a master-slave robot-assisted system is developed for actuation of a steerable catheter. Three-dimensional ultrasound image volumes go through some processing to make the heart tissue and the catheter more visible. The rendered volume is shown in a virtual environment. The catheter can also be added as a virtual tool to this environment to achieve a higher update rate on the catheter's position. The ultrasound probe is also equipped with an EM tracker which is used for online registration of the ultrasound images and the catheter tracking data. The whole augmented reality scene can be shown stereoscopically to enhance depth perception for the user. We have used transthoracic echocardiography (TTE) instead of the conventional transoesophageal (TEE) or intracardiac (ICE) echocardiogram. A beating heart model has been used to perform the experiments. This method can be used both for diagnostic and therapeutic applications as well as training interventional cardiologists.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".