Feasibility Study of Respiratory Motion Modeling Based Correction for MRI-Guided Intracardiac Interventional Procedures
Bibliographic record
Abstract
GOAL: The purpose of this study is to improve the accuracy of interventional catheter guidance during intracardiac procedures. Specifically, the use of preprocedural magnetic resonance roadmap images for interventional guidance has limited anatomical accuracy due to intraprocedural respiratory motion of the heart. Therefore, we propose to build a novel respiratory motion model to compensate for this motion-induced error during magnetic resonance imaging (MRI)-guided procedures. METHODS: We acquire 2-D real-time free-breathing images to characterize the respiratory motion, and build a smooth motion model via registration of 3-D prior roadmap images to the real-time images within a novel principal axes frame of reference. The model is subsequently used to correct the interventional catheter positions with respect to the anatomy of the heart. RESULTS: We demonstrate that the proposed modeling framework can lead to smoother motion models, and potentially lead to more accurate motion estimates. Specifically, MRI-guided intracardiac ablations were performed in six preclinical animal experiments. Then, from retrospective analysis, the proposed motion modeling technique showed the potential to achieve a 27% improvement in ablation targeting accuracy. CONCLUSION: The feasibility of a respiratory motion model-based correction framework has been successfully demonstrated. SIGNIFICANCE: The improvement in ablation accuracy may lead to significant improvements in success rate and patient outcomes for MRI-guided intracardiac procedures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".