Three-Tesla Magnetic Resonance Imaging of Patients With Deep Brain Stimulators: Results From a Phantom Study and a Pilot Study in Patients
Bibliographic record
Abstract
BACKGROUND: Deep brain stimulation (DBS) is a standard of care treatment for multiple neurologic disorders. Although 3-tesla (3T) magnetic resonance imaging (MRI) has become the gold-standard modality for structural and functional imaging, most centers refrain from 3T imaging in patients with DBS devices in place because of safety concerns. 3T MRI could be used not only for structural imaging, but also for functional MRI to study the effects of DBS on neurocircuitry and optimize programming. OBJECTIVE: To use an anthropomorphic phantom design to perform temperature and voltage safety testing on an activated DBS device during 3T imaging. METHODS: An anthropomorphic 3D-printed human phantom was constructed and used to perform temperature and voltage testing on a DBS device during 3T MRI. Based on the phantom assessment, a cohort study was conducted in which 6 human patients underwent MRI with their DBS device in an activated (ON) state. RESULTS: During the phantom study, temperature rises were under 2°C during all sequences, with the DBS in both the deactivated and activated states. Radiofrequency pulses from the MRI appeared to modulate the electrical discharge from the DBS, resulting in slight fluctuations of voltage amplitude. Six human subjects underwent MRI with their DBS in an activated state without any serious adverse events. One patient experienced stimulation-related side effects during T1-MPRAGE scanning with the DBS in an ON state because of radiofrequency-induced modulation of voltage amplitude. CONCLUSION: Following careful phantom-based safety testing, 3T structural and functional MRI can be safely performed in subjects with activated deep brain stimulators.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".