Modeling of a multi-scale electromagnetic problem: pacemaker lead heating in MRI
Bibliographic record
Abstract
Modeling of so-called multi-scale problems is challenging due to large differences between the length scales of\nthe problem, which can be associated with large computational costs. For example, modeling of pacemaker lead\nheating by a 1.5 T magnetic resonance imaging (MRI) system requires length scales differing by a factor 1000 to\nbe resolved. Lead heating is the main cause of the contraindication of pacemakers for MRI [1]. The human body\nsets the size of the largest scale whereas the helically shaped conductors of the pacemaker lead constitute the\nsmallest scale.\n\nTo this date, modeling of these length scales and the heterogeneous body tissue has not been performed\nconcurrently. Neufeld et al. [2] exploited the finite-difference time-domain technique (FDTD) and modeled the\nheating caused by a single helix. However, the associated computational cost prevented them from examining\nmore complex leads. Nevertheless, modern pacing leads often include two helically shaped conductors,\nconsisting of several filars each, whose winding scheme has been shown experimentally to have significant\nimpact on the heating [3].\n\nTherefore, we devote special attention to the multi-scale part of the problem and exploit the frequency-domain method of moments to model an MRI radio frequency coil and a homogeneous human body phantom with an\nimplanted pacemaker system. The pacemaker lead consists of two helically shaped conductors modeled as thin\nwires, insulation, and electrodes modeled by surfaces.\n\nWe exploit the model to assess the effect on the heating by different factors, such as the presence of a pacemaker\nunit. Figure 1 shows the amplification of the absolute value of the electric field with respect to the fields in an\nempty phantom. Furthermore, we study the accuracy of the thin-wire approximation for densely wound helices\nby comparing it to a surface discretization of the helix. Figure 2 shows the maximum value of the induced\ncurrent on a straight helix which is illuminated by a plane wave polarized along the helix axis. The wave\nimpinges at a straight angle to the helix axis.\n\n[1] Götte et al. Magnetic resonance imaging, pacemakers and implantable cardioverter-defibrillators: current\nsituation and clinical perspective. Netherlands heart journal, 18(1):31–7, January 2010.\n[2] Neufeld et al. Measurement, simulation and uncertainty assessment of implant heating during MRI. Physics\nin medicine and biology, 54(13):4151–69, July 2009.\n[3] Bottomley et al. Designing passive MRI-safe implantable conducting leads with electrodes. Medical\nPhysics, 37(7):3828–3843, 2010.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".