Calibration and evaluation of a magnetically tracked ICE probe for guidance of left atrial ablation therapy
Bibliographic record
Abstract
The novel prototype system for advanced visualization for image-guided left atrial ablation therapy developed in our laboratory permits ready integration of multiple imaging modalities, surgical instrument tracking, interventional devices and electro-physiologic data. This technology allows subject-specific procedure planning and guidance using 3D dynamic, patient-specific models of the patient's heart, augmented with real-time intracardiac echocardiography (ICE). In order for the 2D ICE images to provide intuitive visualization for accurate catheter to surgical target navigation, the transducer must be tracked, so that the acquired images can be appropriately presented with respect to the patient-specific anatomy. Here we present the implementation of a previously developed ultrasound calibration technique for a magnetically tracked ICE transducer, along with a series of evaluation methods to ensure accurate imaging and faithful representation of the imaged structures. Using an engineering-designed phantom, target localization accuracy is assessed by comparing known target locations with their transformed locations inferred from the tracked US images. In addition, the 3D volume reconstruction accuracy is also estimated by comparing a truth volume to that reconstructed from sequential 2D US images. Clinically emulating validation studies are conducted using a patient-specific left atrial phantom. Target localization error of clinically-relevant surgical targets represented by nylon fiducials implanted within the endocardial wall of the phantom was assessed. Our studies have demonstrated 2.4 ± 0.8 mm target localization error in the engineering-designed evaluation phantoms, 94.8 ± 4.6 % volume reconstruction accuracy, and 3.1 ± 1.2 mm target localization error in the left atrial-mimicking phantom. These results are consistent with those disseminated in the literature and also with the accuracy constraints imposed by the employed technology and the clinical application.
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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 | 0.002 | 0.007 |
| 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 | 0.001 | 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".