Real-time CMR guidance for intracardiac and great vessel pressure mapping in patients with congenital heart disease using an MR conditional guidewire—results of 25 patients
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to test a CE-certified MR-conditional guidewire to facilitate blood pressure measurement in cardiovascular magnetic resonance (CMR) using fluid-filled catheters in patients with congenital heart disease (CHD). The main purpose was to determine procedural success in a post market clinical follow-up (PMCF) for routine procedure in a diagnostic and interventional workflow. Real-time CMR provides high quality imaging without the risk of exposing the patient to X-rays, especially for patients with irregular heart anatomy and patients who are susceptible to radiation and iodinated contrast media. To date, the assessment of blood pressure gradients is not a common feature of CMR, as these gradients cannot be accurately evaluated in routine CMR. METHODS: Twenty-five CHD patients who were planned for combined clinical CMR and diagnostic and/or interventional catheterization were enrolled in the trial. Prior to inclusion, a specific procedure for catheterization in CMR was defined, encompassing the assessment of pressure and pressure gradients in the heart and great vessels. RESULTS: By the use of an MR-conditional guidewire we successfully measured specific pressure and pressure gradients in up to 92% of cases with liquid-filled catheters which were guided exclusively under CMR guidance. There were no guidewire-related adverse events, and guidewire guidance and manipulation of catheters were successful. CONCLUSIONS: Using a MR-conditional guidewire assists in easily reaching targets in the heart and great vessels and makes the catheter itself visible, so that invasive blood pressure assessment by CMR guidance with liquid-filled catheters can be improved. KEYWORDS: Cardiovascular magnetic resonance (CMR); congenital heart disease (CHD); cardiac catheterization; magnetic resonance; pressure; guidewire.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".