COMPREHENSIVE ANALYSIS OF LENZ EFFECT ON THE ARTIFICIAL HEART VALVES DURING MAGNETIC RESONANCE IMAGING
Bibliographic record
Abstract
Abstract—This work presents results of a comprehensive analysis of the Lenz effect due to motion of artificial heart valves during magnetic resonance imaging. The interaction of rotating metallic heart valves with magnetic fields is studied by performing a time-domain analysis of the corresponding electromagnetic problem. We applied the finite element method (FEM) to solve the T − Ω formulation of Maxwell equations in two cases: first, for metallic disks located in the high intensity homogenous field of the magnet iso-center, and second, disks located in the non-uniform fringe field of the bore entrance. We showed that for valves with full solid disks (such as Starr-Edwards 6500) located in the magnet iso-center, the magnitude of adverse forces can be comparable to the forces applied by the beating heart. However, for rings which consist of multiply connected conductive regions, skin effect and proximity effect counteract, which leads to a diminished magnetic force. Results of this study show that mechanical heart valves with strengthening rings may be considered safe even under ultra-high imaging conditions with field intensities as high as 10T. However, heart valves with full conducting disks should be considered as a contraindication to MR imaging.
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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 | 0.000 | 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) | 0.001 | 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".