Numerical Simulations of Realistic Lead Trajectories and an Experimental Verification Support the Efficacy of Parallel Radiofrequency Transmission to Reduce Heating of Deep Brain Stimulation Implants during MRI
Bibliographic record
Abstract
Abstract Patients with deep brain stimulation (DBS) implants may be subject to heating during MRI due to interaction with excitatory radiofrequency (RF) fields. Parallel RF transmit (pTx) has been proposed to minimize such RF-induced heating in preliminary proof-of-concept studies. The present work evaluates the efficacy of pTx technique on realistic lead trajectories obtained from nine DBS patients. Electromagnetic simulations were performed using 4- and 8-element pTx coils compared with a standard birdcage coil excitation using patient models and lead trajectories obtained by segmentation of computed tomography data. Numerical optimization was performed to minimize local specific absorption rate (SAR) surrounding the implant tip while maintaining spatial homogeneity of the transmitted RF magnetic field (B 1 + ), by varying the input amplitude and phase for each coil element. Local SAR was significantly reduced at the lead tip with both 4-element and 8-element pTx (median decrease of 94% and 97%, respectively), whereas the median coefficient of spatial variation of B 1 + inhomogeneity was moderately increased (30% for 4-element pTx and 20% for 8-element pTx) compared to that of the birdcage coil (17%). Furthermore, the efficacy of optimized 4-element pTx was verified experimentally by imaging a head phantom that included a wire implanted to approximate the worst-case lead trajectory for localized heating, based on the simulations. Negligible temperature elevation was observed at the lead tip, with reasonable image uniformity in the surrounding region. From this experiment and the simulations based on nine DBS patient models, optimized pTx provides a robust approach to minimizing local SAR with respect to lead trajectory.
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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".