Workflow and imaging strategies for real-time MR-guided atrial transseptal puncture
Bibliographic record
Abstract
Interventional cardiac magnetic resonance imaging (iCMR) offers distinct advantages for guiding complex cardiac procedures, including 3D visualization, soft tissue characterization, and avoidance of ionizing radiation. Transseptal puncture (TSP), essential for left heart access, poses specific challenges under MR-guidance. The development of MR-compatible TSP sets comprising non-ferromagnetic sheaths, dilators, and needles, represents a major step toward safe execution of TSP in the MRI environment. This report provides practical, step-by-step guidance for MR-guided TSP, focusing on imaging strategies and integration of advanced 2D and 3D navigation tools. Real-time cine imaging in dedicated planes enables precise localization of the fossa ovalis, confirmation of septal tenting, and avoidance of adjacent structures. Complementary use of a vendor-neutral MR-compatible 3D navigation system allows dynamic catheter tracking within a segmented static 3D anatomical shell, enhancing spatial orientation and procedural accuracy. Feasibility was demonstrated in a porcine model, where an MR-compatible sheath and trackable dilator were successfully navigated to the interatrial septum and TSP was achieved, enabling left atrial (LA) access. Subsequent mapping confirmed catheter positioning within the LA. Remaining challenges include limited guidewire visibility, low image temporal resolution compared with fluoroscopy, and the investigational status of current MR-compatible TSP sets. These factors must be addressed before clinical translation. In conclusion, MR-guided TSP using dedicated imaging planes and MR-compatible devices is technically feasible and may facilitate future radiation-free left heart interventions. Continued device refinement, including improved passive instrument visibility and active tracking technologies, faster real-time cine imaging, and regulatory approval are critical for safe and widespread clinical adoption.
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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 | 0.005 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".