Technical note: System uncertainty on four‐ and eight‐channel parallel RF transmission for safe MRI of deep brain stimulation devices
Bibliographic record
Abstract
BACKGROUND: Parallel radiofrequency transmission (pTx) remains a promising technology for addressing high-field magnetic resonance imaging (MRI) challenges, particularly regarding the safety of patients with implanted deep brain stimulation (DBS) devices. Radiofrequency (RF) shim optimization methods utilizing pTx technology have shown the potential to minimize induced RF heating effects at the electrode tips of DBS devices at 3 T. PURPOSE: Research pTx system implementations often involve the combination of custom and commercial hardware that are integrated onto an existing MRI system. As a result, system characterization is important to ensure implant-friendly safe imaging conditions are satisfied for the operating range of the hardware. METHODS: Utilizing electromagnetic and thermal simulations, the impact of system uncertainty is studied for the proposed 4- and 8-channel pTx system setup and its associated "safe mode" for DBS applications. RESULTS: Electromagnetic simulations indicated that instrumentation errors can affect the overall electric field strength experienced at the DBS lead tip, and a worst-case system uncertainty analysis predicted temperature elevations of +1.5°C in the 4-channel setup and +0.9°C in the 8-channel setup. CONCLUSIONS: In conclusion, system uncertainty can impact the precision of pTx RF inputs which in the worst-case, may lead to an unsafe imaging scenario and the proposed 8-channel setup may provide more robustness and thus, safer conditions for MRI of DBS patients.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".